US20220014555A1 - Distributed automated planning and execution platform for designing and running complex processes - Google Patents
Distributed automated planning and execution platform for designing and running complex processesDownload PDFInfo
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
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- G06F9/5038—Allocation of resources, e.g. of the central processing unit [CPU] to service a request the resource being a machine, e.g. CPUs, Servers, Terminals considering the execution order of a plurality of tasks, e.g. taking priority or time dependency constraints into consideration
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Definitions
- the disclosurerelates to the field of automated tracking and management of collaborative projects.
- Automated planningis a branch of machine learning that focuses on computationally creating ordered sets of actions to perform a given task.
- Artificial intelligencehas been used in this way for decades to plan robotic activity, controlling unmanned vehicles, and to plan manned operations such as space missions.
- current systemsdo not scale well when the number of objects in a given problem space is large.
- Current systemsattempt to deal with this limitation using domain-independent heuristics, however this is an inelegant solution that results in large search trees that cannot be processed in a reasonable time and thus imposing a de facto limit on the size of the problem a given system is capable of handling.
- a platformmay be deployed in a distributed or federated architecture, without the need for strict synchronization across system components and instead relying on an “eventual agreement” model wherein consistency is achieved in an asynchronous, yet certain, manner.
- the systemmay also employ various machine learning techniques and simulations to continually improve and evolve through the use of data transformation pipelines, ensuring the system can scale as needed to handle increasingly complex processes.
- a system for management and tracking of collaborative projectscomprising: an automated planning service comprising a memory, a processor, and a plurality of programming instructions stored in the memory thereof and operable on the processor thereof, wherein the programmable instructions, when operating on the processor, cause the processor to: operate a plurality of master nodes, each master node in turn operating a plurality of worker nodes; receive a plurality of simulation conditions at a master node; construct a plurality of simulation components based on the received simulation conditions; construct a planning model based on the received simulation conditions; assign, using a master node, a plurality of discrete simulation tasks to a plurality of worker nodes, each of the plurality of discrete simulation tasks being based on the constructed simulation components and planning model, wherein each of the plurality of worker nodes is assigned exactly one of the plurality of discrete simulation tasks at any given time during operation; analyze results of each of the plurality of discrete simulation tasks as they are completed; and provide the analyzed results as output, is disclosed
- a method for management and tracking of collaborative projectscomprising the steps of: operating, at an automated planning service, a plurality of master nodes, each master node in turn operating a plurality of worker nodes; receiving a plurality of simulation conditions at a master node; constructing a plurality of simulation components based on the received simulation conditions; constructing a planning model based on the received simulation conditions; assigning, using a master node, a plurality of discrete simulation tasks to a plurality of worker nodes, each of the plurality of discrete simulation tasks being based on the constructed simulation components and planning model, wherein each of the plurality of worker nodes is assigned exactly one of the plurality of discrete simulation tasks at any given time during operation; analyzing results of each of the plurality of discrete simulation tasks as they are completed; and providing the analyzed results as output, is disclosed.
- FIG. 1is a block diagram illustrating an exemplary hardware architecture of a computing device used in various embodiments of the invention.
- FIG. 2is a block diagram illustrating an exemplary logical architecture for a client device, according to various embodiments of the invention.
- FIG. 3is a block diagram illustrating an exemplary architectural arrangement of clients, servers, and external services, according to various embodiments of the invention.
- FIG. 4is a block diagram illustrating an exemplary overview of a computer system as may be used in any of the various locations throughout the system
- FIG. 5is a diagram of an exemplary architecture for a system where streams of input data from one or more of a plurality of sources are analyzed to predict outcome using both batch analysis of acquired data and transformation pipeline manipulation of current streaming data according to an embodiment of the invention.
- FIG. 6is a diagram of an exemplary architecture for a linear transformation pipeline system which introduces the concept of the transformation pipeline as a directed graph of transformation nodes and messages according to an embodiment of the invention.
- FIG. 7is a diagram of an exemplary architecture for a transformation pipeline system where one of the transformations receives input from more than one source which introduces the concept of the transformation pipeline as a directed graph of transformation nodes and messages according to an embodiment of the invention.
- FIG. 8is a diagram of an exemplary architecture for a transformation pipeline system where the output of one data transformation servers as the input of more than one downstream transformation which introduces the concept of the transformation pipeline as a directed graph of transformation nodes and messages according to an embodiment of the invention.
- FIG. 9is a diagram of an exemplary architecture for a transformation pipeline system where a set of three data transformations act to form a cyclical pipeline which also introduces the concept of the transformation pipeline as a directed graph of transformation nodes and messages according to an embodiment of the invention.
- FIG. 10is a process flow diagram of a method for the receipt, processing and predictive analysis of streaming data using a system of the invention.
- FIG. 11is a process flow diagram of a method for representing the operation of the transformation pipeline as a directed graph function using a system of the invention.
- FIG. 12is a process flow diagram of a method for a linear data transformation pipeline using a system of the invention.
- FIG. 13is a process flow diagram of a method for the disposition of input from two antecedent data transformations into a single data transformation of transformation pipeline using a system of the invention.
- FIG. 14is a process flow diagram of a method for the disposition of output of one data transformation that then serves as input to two postliminary data transformations using a system of the invention.
- FIG. 15is a process flow diagram of a method for processing a set of three or more data transformations within a data transformation pipeline where output of the last member transformation of the set serves as input of the first member transformation thereby creating a cyclical relationship using a system of the invention.
- FIG. 16is a process flow diagram of a method for the receipt and use of streaming data into batch storage and analysis of changes over time, repetition of specific data sequences or the presence of critical data points using a system of the invention.
- FIG. 17is a diagram of a computing architecture for a processing system according to one aspect of the present invention.
- FIG. 18is a diagram of a computing pipeline architecture for a processing system according to one aspect of the present invention.
- FIG. 19is a diagram of a computing operating states for a processing system according to one aspect of the present invention.
- FIG. 20A-20Dis a process flow diagram for a set of processing operations used in a pipeline processing system according to one aspect of the present invention.
- FIG. 21is a system diagram detailing the components of a Production Rule System (PRS), according to an embodiment.
- PRSProduction Rule System
- FIG. 22is a system diagram illustrating cyclic workflow stages in a pipeline of data analysis, according to an embodiment.
- FIG. 23is a block diagram illustrating an exemplary system architecture for automated planning, according to a preferred embodiment.
- FIG. 24is a flow diagram illustrating an exemplary overview of a process for automated planning, according to a preferred embodiment.
- FIG. 25is a flow diagram illustrating an exemplary process for a single-run AP job.
- FIG. 26is a block diagram illustrating an exemplary process for a multiple-run AP job.
- the inventorhas conceived, and reduced to practice, a distributed automated planning and execution platform for designing, instantiating, and running complex and evolving processes.
- Devices that are in communication with each otherneed not be in continuous communication with each other, unless expressly specified otherwise.
- devices that are in communication with each othermay communicate directly or indirectly through one or more intermediaries, logical or physical.
- stepsmay be performed simultaneously despite being described or implied as occurring sequentially (e.g., because one step is described after the other step).
- the illustration of a process by its depiction in a drawingdoes not imply that the illustrated process is exclusive of other variations and modifications thereto, does not imply that the illustrated process or any of its steps are necessary to one or more of the invention(s), and does not imply that the illustrated process is preferred.
- stepsare generally described once per embodiment, but this does not mean they must occur once, or that they may only occur once each time a process, method, or algorithm is carried out or executed. Some steps may be omitted in some embodiments or some occurrences, or some steps may be executed more than once in a given embodiment or occurrence.
- graphis a representation of information and relationships, where each primary unit of information makes up a “node” or “vertex” of the graph and the relationship between two nodes makes up an edge of the graph.
- Nodescan be further qualified by the connection of one or more descriptors or “properties” to that node. For example, given the node “James R,” name information for a person, qualifying properties might be “183 cm tall”, “DOB Aug. 13, 1965” and “speaks English”. Similar to the use of properties to further describe the information in a node, a relationship between two nodes that forms an edge can be qualified using a “label”.
- transformation graphswhich are highly variable in size and node, edge composition as the system processes data streams.
- transformation graphmay assume many shapes and sizes with a vast topography of edge relationships. The examples given were chosen for illustrative purposes only and represent a small number of the simplest of possibilities. These examples should not be taken to define the possible graphs expected as part of operation of the invention
- transformationis a function performed on zero or more streams of input data which results in a single stream of output which may or may not then be used as input for another transformation. Transformations may comprise any combination of machine, human or machine-human interactions Transformations need not change data that enters them, one example of this type of transformation would be a storage transformation which would receive input and then act as a queue for that data for subsequent transformations. As implied above, a specific transformation may generate output data in the absence of input data. A time stamp serves as an example. In the invention, transformations are placed into pipelines such that the output of one transformation may serve as an input for another. These pipelines can consist of two or more transformations with the number of transformations limited only by the resources of the system.
- transformation pipelineshave been linear with each transformation in the pipeline receiving input from one antecedent and providing output to one subsequent with no branching or iteration.
- Other pipeline configurationsare possible.
- the inventionis designed to permit several of these configurations including, but not limited to: linear, afferent branch, efferent branch and cyclical.
- a “database” or “data storage subsystem”(these terms may be considered substantially synonymous), as used herein, is a system adapted for the long-term storage, indexing, and retrieval of data, the retrieval typically being via some sort of querying interface or language.
- “Database”may be used to refer to relational database management systems, but should not be considered to be limited to such systems.
- Many alternative database or data storage system technologieshave been, and indeed are being, introduced, including but not limited to distributed non-relational data storage systems such as Hadoop, column-oriented databases, in-memory databases, and the like.
- any data storage architecturemay be used according to the embodiments.
- one or more particular data storage needsare described as being satisfied by separate components (for example, an expanded private capital markets database and a configuration database), these descriptions refer to functional uses of data storage systems and do not refer to their physical architecture.
- any group of data storage systems of databases referred to hereinmay be included together in a single database management system operating on a single machine, or they may be included in a single database management system operating on a cluster of machines.
- any single database(such as an expanded private capital markets database) may be implemented on a single machine, on a set of machines using clustering technology, on several machines connected by one or more messaging systems, or in a master/slave arrangement.
- the techniques disclosed hereinmay be implemented on hardware or a combination of software and hardware. For example, they may be implemented in an operating system kernel, in a separate user process, in a library package bound into network applications, on a specially constructed machine, on an application-specific integrated circuit (ASIC), or on a network interface card.
- ASICapplication-specific integrated circuit
- Software/hardware hybrid implementations of at least some of the embodiments disclosed hereinmay be implemented on a programmable network-resident machine (which should be understood to include intermittently connected network-aware machines) selectively activated or reconfigured by a computer program stored in memory.
- a programmable network-resident machinewhich should be understood to include intermittently connected network-aware machines
- Such network devicesmay have multiple network interfaces that may be configured or designed to utilize different types of network communication protocols.
- a general architecture for some of these machinesmay be disclosed herein in order to illustrate one or more exemplary means by which a given unit of functionality may be implemented.
- At least some of the features or functionalities of the various embodiments disclosed hereinmay be implemented on one or more general-purpose computers associated with one or more networks, such as for example an end-user computer system, a client computer, a network server or other server system possibly networked with others in a data processing center, a mobile computing device (e.g., tablet computing device, mobile phone, smartphone, laptop, and the like), a consumer electronic device, a music player, or any other suitable electronic device, router, switch, or the like, or any combination thereof.
- at least some of the features or functionalities of the various embodiments disclosed hereinmay be implemented in one or more virtualized computing environments (e.g., network computing clouds, virtual machines hosted on one or more physical computing machines, or the like).
- Computing device 100may be, for example, any one of the computing machines listed in the previous paragraph, or indeed any other electronic device capable of executing software- or hardware-based instructions according to one or more programs stored in memory.
- Computing device 100may be adapted to communicate with a plurality of other computing devices, such as clients or servers, over communications networks such as a wide area network a metropolitan area network, a local area network, a wireless network, the Internet, or any other network, using known protocols for such communication, whether wireless or wired.
- communications networkssuch as a wide area network a metropolitan area network, a local area network, a wireless network, the Internet, or any other network, using known protocols for such communication, whether wireless or wired.
- computing device 100includes one or more central processing units (CPU) 102 , one or more interfaces 110 , and one or more buses 106 (such as a peripheral component interconnect (PCI) bus).
- CPU 102may be responsible for implementing specific functions associated with the functions of a specifically configured computing device or machine.
- a computing device 100may be configured or designed to function as a server system utilizing CPU 102 , local memory 101 and/or remote memory 120 , and interface(s) 110 .
- CPU 102may be caused to perform one or more of the different types of functions and/or operations under the control of software modules or components, which for example, may include an operating system and any appropriate applications software, drivers, and the like.
- CPU 102may include one or more processors 103 such as, for example, a processor from one of the Intel, ARM, Qualcomm, and AMD families of microprocessors.
- processors 103may include specially designed hardware such as application-specific integrated circuits (ASICs), electrically erasable programmable read-only memories (EEPROMs), field-programmable gate arrays (FPGAs), and so forth, for controlling operations of computing device 100 .
- ASICsapplication-specific integrated circuits
- EEPROMselectrically erasable programmable read-only memories
- FPGAsfield-programmable gate arrays
- a local memory 101such as non-volatile random access memory (RAM) and/or read-only memory (ROM), including for example one or more levels of cached memory
- RAMnon-volatile random access memory
- ROMread-only memory
- Memory 101may be used for a variety of purposes such as, for example, caching and/or storing data, programming instructions, and the like.
- processoris not limited merely to those integrated circuits referred to as a processor, a mobile processor, or a microprocessor, but broadly refers to a microcontroller, a microcomputer, a programmable logic controller, an application-specific integrated circuit, and any other programmable circuit.
- interfaces 110are provided as network interface cards (NICs).
- NICscontrol the sending and receiving of data packets over a computer network; other types of interfaces 110 may for example support other peripherals used with computing device 100 .
- the interfacesthat may be provided are Ethernet interfaces, frame relay interfaces, cable interfaces, DSL interfaces, token ring interfaces, graphics interfaces, and the like.
- interfacesmay be provided such as, for example, universal serial bus (USB), Serial, Ethernet, Firewire, PCI, parallel, radio frequency (RF), Bluetooth, near-field communications (e.g., using near-field magnetics), 802.11 (WiFi), frame relay, TCP/IP, ISDN, fast Ethernet interfaces, Gigabit Ethernet interfaces, asynchronous transfer mode (ATM) interfaces, high-speed serial interface (HSSI) interfaces, Point of Sale (POS) interfaces, fiber data distributed interfaces (FDDIs), and the like.
- USBuniversal serial bus
- RFradio frequency
- FIG. 1illustrates one specific architecture for a computing device 100 for implementing one or more of the inventions described herein, it is by no means the only device architecture on which at least a portion of the features and techniques described herein may be implemented.
- architectures having one or any number of processors 103may be used, and such processors 103 may be present in a single device or distributed among any number of devices.
- a single processor 103handles communications as well as routing computations, while in other embodiments a separate dedicated communications processor may be provided.
- different types of features or functionalitiesmay be implemented in a system according to the invention that includes a client device (such as a tablet device or smartphone running client software) and server systems (such as a server system described in more detail below).
- the system of the present inventionmay employ one or more memories or memory modules (such as, for example, remote memory block 120 and local memory 101 ) configured to store data, program instructions for the general-purpose network operations, or other information relating to the functionality of the embodiments described herein (or any combinations of the above).
- Program instructionsmay control execution of or comprise an operating system and/or one or more applications, for example.
- Memory 120 or memories 101 , 120may also be configured to store data structures, configuration data, encryption data, historical system operations information, or any other specific or generic non-program information described herein.
- At least some network device embodimentsmay include nontransitory machine-readable storage media, which, for example, may be configured or designed to store program instructions, state information, and the like for performing various operations described herein.
- nontransitory machine-readable storage mediainclude, but are not limited to, magnetic media such as hard disks, floppy disks, and magnetic tape; optical media such as CD-ROM disks; magneto-optical media such as optical disks, and hardware devices that are specially configured to store and perform program instructions, such as read-only memory devices (ROM), flash memory, solid state drives, memristor memory, random access memory (RAM), and the like.
- program instructionsinclude both object code, such as may be produced by a compiler, machine code, such as may be produced by an assembler or a linker, byte code, such as may be generated by for example a Java compiler and may be executed using a Java virtual machine or equivalent, or files containing higher level code that may be executed by the computer using an interpreter (for example, scripts written in Python, Perl, Ruby, Groovy, or any other scripting language).
- object codesuch as may be produced by a compiler
- machine codesuch as may be produced by an assembler or a linker
- byte codesuch as may be generated by for example a Java compiler and may be executed using a Java virtual machine or equivalent
- files containing higher level codethat may be executed by the computer using an interpreter (for example, scripts written in Python, Perl, Ruby, Groovy, or any other scripting language).
- systems according to the present inventionmay be implemented on a standalone computing system.
- FIG. 2there is shown a block diagram depicting a typical exemplary architecture of one or more embodiments or components thereof on a standalone computing system.
- Computing device 200includes processors 210 that may run software that carry out one or more functions or applications of embodiments of the invention, such as for example a client application 230 .
- Processors 210may carry out computing instructions under control of an operating system 220 such as, for example, a version of Microsoft's Windows operating system, Apple's Mac OS/X or iOS operating systems, some variety of the Linux operating system, Google's Android operating system, or the like.
- an operating system 220such as, for example, a version of Microsoft's Windows operating system, Apple's Mac OS/X or iOS operating systems, some variety of the Linux operating system, Google's Android operating system, or the like.
- one or more shared services 225may be operable in system 200 , and may be useful for providing common services to client applications 230 .
- Services 225may for example be Windows services, user-space common services in a Linux environment, or any other type of common service architecture used with operating system 210 .
- Input devices 270may be of any type suitable for receiving user input, including for example a keyboard, touchscreen, microphone (for example, for voice input), mouse, touchpad, trackball, or any combination thereof.
- Output devices 260may be of any type suitable for providing output to one or more users, whether remote or local to system 200 , and may include for example one or more screens for visual output, speakers, printers, or any combination thereof.
- Memory 240may be random-access memory having any structure and architecture, for use by processors 210 , for example to run software.
- Storage devices 250may be any magnetic, optical, mechanical, memristor, or electrical storage device for storage of data in digital form. Examples of storage devices 250 include flash memory, magnetic hard drive, CD-ROM, and/or the like.
- systems of the present inventionmay be implemented on a distributed computing network, such as one having any number of clients and/or servers.
- FIG. 3there is shown a block diagram depicting an exemplary architecture 300 for implementing at least a portion of a system according to an embodiment of the invention on a distributed computing network.
- any number of clients 330may be provided.
- Each client 330may run software for implementing client-side portions of the present invention; clients may comprise a system 200 such as that illustrated in FIG. 2 .
- any number of servers 320may be provided for handling requests received from one or more clients 330 .
- Clients 330 and servers 320may communicate with one another via one or more electronic networks 310 , which may be in various embodiments of the Internet, a wide area network, a mobile telephony network, a wireless network (such as WiFi, Wimax, and so forth), or a local area network (or indeed any network topology; the invention does not prefer any one network topology over any other).
- Networks 310may be implemented using any known network protocols, including for example wired and/or wireless protocols.
- servers 320may call external services 370 when needed to obtain additional information, or to refer to additional data concerning a particular call. Communications with external services 370 may take place, for example, via one or more networks 310 .
- external services 370may comprise web-enabled services or functionality related to or installed on the hardware device itself. For example, in an embodiment where client applications 230 are implemented on a smartphone or other electronic device, client applications 230 may obtain information stored in a server system 320 in the cloud or on an external service 370 deployed on one or more of a particular enterprise's or user's premises.
- clients 330 or servers 320may make use of one or more specialized services or appliances that may be deployed locally or remotely across one or more networks 310 .
- one or more databases 340may be used or referred to by one or more embodiments of the invention. It should be understood that databases 340 may be arranged in a wide variety of architectures and using a wide variety of data access and manipulation means.
- one or more databases 340may comprise a relational database system using a structured query language (SQL), while others may comprise an alternative data storage technology such as “NoSQL” (for example, Hadoop, MapReduce, BigTable, and so forth).
- SQLstructured query language
- database architecturessuch as column-oriented databases, in-memory databases, clustered databases, distributed databases, key-value stores, or even flat file data repositories may be used according to the invention.
- databaseany combination of database technologies may be used as appropriate, unless a specific database technology or a specific arrangement of components is specified for a particular embodiment herein.
- databasemay refer to a physical database machine, a cluster of machines acting as a single database system, or a logical database within an overall database management system. Unless a specific meaning is specified for a given use of the term “database”, it should be construed to mean any of these senses of the word, all of which are understood as a plain meaning of the term “database”.
- security and configuration managementare common information technology (IT) and web functions, and some amount of each are generally associated with any IT or web systems. It should be understood that any configuration or security subsystems may be used in conjunction with embodiments of the invention without limitation, unless a specific security 360 or configuration 350 system or approach is specifically required by the description of any specific embodiment.
- FIG. 4shows an exemplary overview of a computer system 400 as may be used in any of the various locations throughout the system. It is exemplary of any computer that may execute code to process data. Various modifications and changes may be made to computer system 400 without departing from the broader scope of the system and method disclosed herein.
- CPU 401is connected to bus 402 , to which bus is also connected memory 403 , nonvolatile memory 404 , display 407 , I/O unit 408 , and network interface card (NIC) 413 .
- I/O unit 408may, typically, be connected to keyboard 409 , pointing device 410 , hard disk 412 , and real-time clock 411 .
- NIC 413connects to network 414 , which may be the Internet or a local network, which local network may or may not have connections to the Internet. Also shown as part of system 400 is power supply unit 405 connected, in this example, to ac supply 406 . Not shown are batteries that could be present, and many other devices and modifications that are well known but are not applicable to the specific novel functions of the current system and method disclosed herein.
- functionality for implementing systems or methods of the present inventionmay be distributed among any number of client and/or server components.
- various software modulesmay be implemented for performing various functions in connection with the present invention, and such modules may be variously implemented to run on server and/or client components.
- FIG. 23is a block diagram illustrating an exemplary system architecture for automated planning, according to a preferred embodiment.
- an automated planning (AP) system 2300is capable of handling queue-based jobs individually or in batches as multiple-run jobs, with individual worker nodes handling tasks and providing completed work to a master node that evaluates and publishes results, for example using an AKKATM cluster or similar master/worker cluster operation.
- Master and worker nodesmay be distributed across a network, and may operate as a federated service that may be accessible to clients from any location, providing a cloud-based AP service.
- An automated planning (AP) service 2320connects to a plurality of databases 2310 a - n , such as a MDTSD for storing time-series data or various query-specific datastores such as (for example, including but not limited to) a SQL or GraphStack storage, or other data storage suitable for storing and providing data to various components of the system as needed.
- Datamay be provided to AP service 2320 via a REST API 2311 to enable initiation, processing, and retrieval of data by AP service 2320 .
- AP service 2320in turn comprises a plurality of worker nodes 2321 a - n that are managed by one of a plurality of master nodes 2322 a - n , which receives data from databases 2310 a - n as well as via REST API 2323 from external inputs such as (for example) a user interacting via a command-line interface (CLI) 2330 .
- CLIcommand-line interface
- Master nodes 2322 a - nprovide work tasks to worker nodes 2321 a - n , and worker nodes 2321 a - n operate only to complete a given work task.
- a worker nodereports to its corresponding master node that it is ready for work, and when a processing task completes the worker node publishes the results to its master node along with a report that it is again ready for new work.
- each individual worker nodeoperates independently to focus on a delegated work task, while master nodes delegate work and collect completed items.
- FIG. 5is a block diagram of an exemplary architecture for a system 500 for predictive analysis of very large data sets using a distributed computational graph (DCG).
- streaming input feeds 510may be a variety of data sources which may include but are not limited to the internet 511 , arrays of physical sensors 512 , database servers 513 , electronic monitoring equipment 514 and direct human interaction 515 ranging from a relatively few numbers of participants to a large crowd sourcing campaign. Streaming data from any combinations of listed sources and those not listed may also be expected to occur as part of the operation of the invention as the number of streaming input sources is not limited by the design.
- All incoming streaming datamay be passed through a data filter software module 520 to remove information that has been damaged in transit, is misconfigured, or is malformed in some way that precludes use.
- Many of the filter parametersmay be expected to be preset prior to operation, however, design of the invention makes provision for the behavior of the filter software module 520 to be changed as progression of analysis requires through the automation of the system sanity and retrain software module 563 which may serve to optimize system operation and analysis function.
- the data streammay also be split into two identical sub streams at the data filter software module 520 with one sub stream being fed into a streaming analysis pathway that includes the transformation pipeline software module 561 of the distributed computational graph 560 .
- the other sub streammay be fed to data formalization software module 530 as part of the batch analysis pathway.
- the data formalization module 530formats the data stream entering the batch analysis pathway of the invention into data records to be stored by the input event data store 540 .
- the input event data store 540can be a database of any architectural type, but based upon the data model the data store module would be expected to store and retrieve, options using highly distributed storage and map reduce query protocols, of which Hadoop is one, but not the only example, may be generally preferable to relational database schema.
- Analysis of data from the input event data storemay be performed by the batch event analysis software module 550 .
- This modulemay be used to analyze the data in the input event data store for temporal information such as trends, previous occurrences of the progression of a set of events, with outcome, the occurrence of a single specific event with all events recorded before and after whether deemed relevant at the time or not, and presence of a particular event with all documented possible causative and remedial elements, including best guess probability information. It should be recognized that while examples here focus on having stores of information pertaining to time, the use of the invention is not limited to such contexts as there are other fields where having a store of existing data would be critical to predictive analysis of streaming data 561 .
- the search parameters used by the batch event analysis software module 550are preset by those conducting the analysis at the beginning of the process, however, as the search matures and results are gleaned from the streaming data during transformation pipeline software module 561 operation, providing the system more timely event progress details, the system sanity and retrain software module 563 may automatically update the batch analysis parameters 550 . Alternately, findings outside the system may precipitate the authors of the analysis to tune the batch analysis parameters administratively from outside the system 570 , 562 , 563 .
- the real-time data analysis core 560 of the inventionshould be considered made up of a transformation pipeline software module 561 , messaging module 562 and system sanity and retrain software module 563 .
- the messaging module 562has connections from both the batch and the streaming data analysis pathways and serves as a conduit for operational as well as result information between those two parts of the invention.
- the message modulealso receives messages from those administering analyses 580 .
- Messages aggregated by the messaging module 562may then be sent to system sanity and retrain software module 563 as appropriate.
- thisis software that may be used to monitor the progress of streaming data analysis optimizing coordination between streaming and batch analysis pathways by modifying or “retraining” the operation of the data filter software module 520 , data formalization software module 530 and batch event analysis software module 540 and the transformation pipeline module 550 of the streaming pathway when the specifics of the search may change due to results produced during streaming analysis.
- System sanity and retrain module 563may also monitor for data searches or transformations that are processing slowly or may have hung and for results that are outside established data stability boundaries so that actions can be implemented to resolve the issue. While the system sanity and retrain software module 563 may be designed to act autonomously and employs computer learning algorithms, according to some arrangements status updates may be made by administrators or potentially direct changes to operational parameters by such, according to the embodiment.
- Streaming data entering from the outside data feeds 510 through the data filter software module 520may be analyzed in real time within the transformation pipeline software module 561 .
- a set of functions tailored to the analysis being runare applied to the input data stream.
- functionsmay be applied in a linear, directed path or in more complex configurations.
- Functionsmay be modified over time during an analysis by the system sanity and retrain software module 563 and the results of the transformation pipeline, impacted by the results of batch analysis are then output in the format stipulated by the authors of the analysis which may be human readable printout, an alarm, machine readable information destined for another system or any of a plurality of other forms.
- FIG. 6is a block diagram of a preferred architecture for a transformation pipeline within a system for predictive analysis of very large data sets using distributed computational graph 600 .
- streaming input from the data filter software module 520 , 615serves as input to the first transformation node 620 of the transformation pipeline.
- Transformation node's functionis performed on input data stream and transformed output message 625 is sent to transformation node 2 630 .
- the progression of transformation nodes 620 , 630 , 640 , 650 , 660 and associated output messages from each node 625 , 635 , 645 , 655 , 665is linear in configuration this is the simplest arrangement and, as previously noted, represents the current state of the art.
- transformation nodesare described according to various embodiments as uniform shape (referring to FIGS. 6-9 ), such uniformity is used for presentation simplicity and clarity and does not reflect necessary operational similarity between transformations within the pipeline. It should be appreciated that one knowledgeable in the field will realize that certain transformations in a pipeline may be entirely self-contained; certain transformations may involve direct human interaction 630 , such as selection via dial or dials, positioning of switch or switches, or parameters set on control display, all of which may change during analysis; other transformations may require external aggregation or correlation services or may rely on remote procedure calls to synchronous or asynchronous analysis engines as might occur in simulations among a plurality of other possibilities. Further according to the embodiment, individual transformation nodes in one pipeline may represent function of another transformation pipeline.
- the node length of transformation pipelines depicted in no wayconfines the transformation pipelines employed by the invention to an arbitrary maximum length 640 , 650 , 660 as, being distributed, the number of transformations would be limited by the resources made available to each implementation of the invention. It should be further appreciated that there need be no limits on transform pipeline length. Output of the last transformation node and by extension, the transform pipeline 660 may be sent back to messaging software module 562 for predetermined action.
- FIG. 7is a block diagram of another preferred architecture for a transformation pipeline within a system for predictive analysis of very large data sets using distributed computational graph 700 .
- streaming input from a data filter software module 520 , 705serves as input to the first transformation node 710 of the transformation pipeline. Transformation node's function is performed on input data stream and transformed output message 715 is sent to transformation node 2 720 .
- transformation node 2 720has a second input stream 765 .
- the specific source of this inputis inconsequential to the operation of the invention and could be another transformation pipeline software module, a data store, human interaction, physical sensors, monitoring equipment for other electronic systems or a stream from the internet as from a crowdsourcing campaign, just to name a few possibilities 760 .
- a second input stream 760may contain a specification of data context that is preserved from the first stream into a node 2 720 , the shared data context between the inputs of a transformation node 720 allowing the services or streams that send data to a node to share common meaning and enable faster or different methods of processing, including finding correlations or causative tendencies between data from two sources or streams, in the case of a shared data context. It is not required that a secondary, tertiary, or further source of data 760 be functioning as input to specifically the second node in the graph 720 , and there may be a plurality of other datastreams feeding into one or several of different nodes in the graph.
- transformation nodesare described according to various embodiments as uniform shape (referring to FIGS. 6-9 ), such uniformity is used for presentation simplicity and clarity and does not reflect necessary operational similarity between transformations within the pipeline.
- transformations in a pipelinemay be entirely self-contained; certain transformations may involve direct human interaction 630 , such as selection via dial or dials, positioning of switch or switches, or parameters set on control display, all of which may change during analysis; other transformations may require external aggregation or correlation services or may rely on remote procedure calls to synchronous or asynchronous analysis engines as might occur in simulations among a plurality of other possibilities. Further according to the embodiment, individual transformation nodes in one pipeline may represent function of another transformation pipeline.
- FIG. 8is a block diagram of another preferred architecture for a transformation pipeline within a system for predictive analysis of very large data sets using distributed computational graph 700 .
- streaming input from a data filter software module 520 , 805serves as input to the first transformation node 810 of the transformation pipeline.
- Transformation node's functionis performed on input data stream and transformed output message 815 is sent to transformation node 2 820 .
- transformation node 2 820sends its output stream 825 , 860 to two transformation pipelines 830 , 840 , 850 ; 865 , 875 .
- transformations in pipelineswhich may be entirely self-contained; certain transformations may involve direct human interaction 630 , such as selection via dial or dials, positioning of switch or switches, or parameters set on control display, all of which may change during analysis; other transformations may require external aggregation or correlation services or may rely on remote procedure calls to synchronous or asynchronous analysis engines as might occur in simulations, among a plurality of other possibilities.
- individual transformation nodes in one pipelinemay represent function of another transformation pipeline.
- the node number of transformation pipelines and their outputs 815 , 825 , 835 , 845 , 855 , 860 , 870 , 880 depicted in no wayconfines the transformation pipelines employed by the invention to an arbitrary maximum length 810 , 820 , 830 , 840 , 850 ; 865 , 875 as, being distributed, the number of transformations would be limited by the resources made available to each implementation of the invention. Further according to the embodiment, there need be no limits on transform pipeline length. Output of the last transformation node and by extension, the transform pipeline 850 may be sent back to messaging software module 562 for contemporary enabled action.
- FIG. 9is a block diagram of another preferred architecture for a transformation pipeline within a system for predictive analysis of very large data sets using distributed computational graph 700 .
- streaming input from a data filter software module 520 , 905serves as input to the first transformation node 910 of the transformation pipeline. Transformation node's function may be performed on an input data stream and transformed output message 915 may then be sent to transformation node 2 920 .
- transformation node 3 930sends its output stream back to transform node 1 935 , 910 forming a cyclical relationship between transformation nodes 1 910 , transformation node 2 920 and transformation node 3 930 .
- the output of cyclical pipeline activitymay be sent to downstream transformation nodes within the pipeline 940 , 945 .
- the presence of a generalized cyclical pathway constructallows the invention to be used to solve complex iterative problems with large data sets involved, expanding ability to rapidly retrieve conclusions for complicated issues. Functional creation of a cyclical transformation pipeline requires that each cycle be serialized.
- the inventionperforms this serialization using a decomposable transformation software module (not shown), the function of which is described below, referring to FIG. 15 . While transformation nodes are described according to various embodiments as uniform shape (referring to FIGS. 6-9 ), such uniformity is used for presentation simplicity and clarity and does not reflect necessary operational similarity between transformations within the pipeline.
- transformations in pipelinesmay be entirely self-contained; certain transformations may involve direct human interaction 630 , such as selection via dial or dials, positioning of switch or switches, or parameters set on control display, all of which may change during analysis; still other transformations may require external aggregation or correlation services or may rely on remote procedure calls to synchronous or asynchronous analysis engines as might occur in simulations, among a plurality of other possibilities. Further according to the embodiment, individual transformation nodes in one pipeline may represent the cumulative function of another transformation pipeline.
- the node number of transformation pipelines depicted in no wayconfines the transformation pipelines employed by the invention to an arbitrary maximum length 910 , 920 , 930 , 940 , 950 ; 965 , 975 as, being distributed, the number of transformations would be limited by the resources made available to each implementation of the invention. It should be further appreciated that there need be no limits on transform pipeline length. Output of the last transformation node 960 and by extension, the transform pipeline 955 may be sent back to messaging software module 562 for concomitant enabled action.
- FIG. 17is a diagram of a computing architecture for a processing system according to one aspect of the present invention.
- An environmental orchestration and data processing engine 1700permits domain experts to directly capture their knowledge via a user interface with domain agnostic building blocks. These modular components can be built and extended by programmers to satisfy a number of use cases without a need to understand how they will be used in a specific implementation.
- An Environmental Orchestration component 1711 and Data Processing component 1712coupled together 1701 , allow for both flexibility and tight coupling between all the actions needed to set up resources and perform analytical tasks.
- the processing tasksare divided between data processing, orchestration, and system tasks.
- the data processing tasksprovide a plug and play style data processing backend and orchestrates work against that backend.
- a data management backend 1702provides the backend processing functionality that consumes data streams for processing.
- a variety of data management and stream processing backendsmay be utilized, including APACHE FLINKTM, SPARKTM, and APACHE BEAMTM.
- JSONJavaScript Object Notation
- a data management backend 1702is a framework and distributed processing engine for stateful computations over unbounded and bounded data streams.
- a data management backend 1702has been designed to run in all common cluster environments, perform computations at in-memory speed and at any scale.
- a data management backend's architecturemay use both Process Unbounded and Bounded Data. Any kind of data is produced as a stream of events. Credit card transactions, sensor measurements, machine logs, or user interactions on a website or mobile application, all of these data are generated as a stream. Data can be processed as unbounded or bounded streams.
- Unbounded streamshave a start but no defined end. They do not terminate and provide data as it is generated. Unbounded streams must be continuously processed, i.e., events must be promptly handled after they have been ingested. It is not possible to wait for all input data to arrive because the input is unbounded and will not be complete at any point in time. Processing unbounded data often requires that events are ingested in a specific order, such as the order in which events occurred, to be able to reason about result completeness.
- Bounded streamshave a defined start and end. Bounded streams can be processed by ingesting all data before performing any computations. Ordered ingestion is not required to process bounded streams because a bounded data set can always be sorted. Processing of bounded streams is also known as batch processing. Pipelines and stages herein may utilize both types of data.
- Orchestration tasksdirectly handle serializing the Pipeline and Stages, monitoring of active Pipelines and submission of new Pipelines, as well as making requests to 3rd parties for resources to be allocated as needed.
- These resourcesmay be provided within a single system, a collections of interconnected processing systems operating together within a data centers, and cloud based resources provided by parties over the internet such as Amazon Web Services and Microsoft Azure. All similar could computing services may be used to provide all or part of a pipeline's stages as needed with data being transferred by addressing the particular resources by its IP address.
- System tasksinclude monitoring, metadata and recovery tasks to provide hooks between a pipeline and the controlling system 1703 itself to enable it to monitor, pull metadata about multiple pipelines running in sequence and facilitate recovery when pipelines fail, or services that fail. These tasks are needed because the controlling system 1703 does not possess a direct feed into the data as it is being processed.
- APACHE FLINKTMis one of many streaming data processing engines
- APIs used to construct the statestypically provide functionality that is extensible enough to utilize other processing engines of streaming data such as SPARKTM, APACHE BEAMTM, and similar stream data processing engines.
- data sinks and data sourcesmay occur any place in the directed graph.
- Each data sink and data sourcemaybe specified by a declarative formalism embodiment within a workflow such that an entire orchestration workflow may be expressed within the overall workflow.
- This architecturepermits the various stages in a workflow to be modularly constructed in which each stage is separately implemented using a declarative definition of a streaming analytics processing workflow. As long as a stage accepts and consumes and then generates and produces a data stream in a common format, any implementation of a particular stage may be used.
- FIG. 18is a diagram of a computing pipeline architecture for a processing system according to one aspect of the present invention.
- the unit by which this is measuredis a Pipeline 1800 .
- a pipeline 1800represents a use case and is the high level application. Different pipelines, as well as components within a pipeline, can work in tandem, allowing for even larger logical applications to be made.
- Pipelinesare constructed of more primitive types called stages.
- a pipeline shown in FIG. 18illustrates a sequence of stages running in parallel. All incoming state is obtained by a source stage, stage 1 1801 . This data is provided to three separate sequences of stages, stage 3 - 4 1811 - 1812 , stage 2 1802 , and stages 5 - 7 1821 - 1823 . Each of these stages may be processing and sink stages as three sets of results are generated.
- a pipeline 1800is defined as a computing structure for housing for all the Stages used to construct the pipeline, where the pipeline of stages is represented as a DG (Directed Graph). This has three basic states, running, suspended, deleted. The difference between suspended and deleted is that the suspended state stops processing but doesn't trigger the post conditions, while deleted stops the processing and triggers the post conditions. Pipelines are comprised of four types of Stages.
- Pipeline 1800may also be constructed using cyclic workflows of stages 3 - 4 1811 - 1812 and stages 15 - 16 1831 - 1832 . These cyclic workflows may be created using the same messaging fabric in a source/sink used to define all other workflows. This arrangement makes the expressive capability of this streaming analytics engine a full directed graph rather than merely directed acyclic graphs of competing formalisms.
- One possible example of a cyclewould be to have a source stage that consumes from a Kafka topic while a separate sink stages passes messages to the same Kafka topic, thus creating a cycle.
- An alternative arrangement and use of the workflow stagesis to functionally decompose the workflow stages, and allow them to be embedded in other workflows as single stages, for instance having a workflow with steps A, B, C, and D, embedding another 3-step workflow with steps E, F, and G, inbetween steps B and C, such that the first workflow of processing data is now comprised of steps A, B, (E, F, G), C, and D.
- This modularity and functional decomposition of data workflowscomprises a possible alternative arrangement of the disclosed system, but is not limiting or the only alternative arrangement that may be possible.
- Environmental Conditionscorrespond parameter and processing conditions a stage is going to need exist to be able to run in processing components. This also includes the reverse process. These are known as the Setup and Teardown Phase. These Conditions are defined by the Stage itself. Environment Stages are a specialized type of stage that contains only these post-conditions and pre-conditions.
- Stagesa simple processing task before the processing of a particular set of data is passed to another stage to perform a next step in the process.
- This architectureprovides separate units of work that may be arranged conceptually for users of this system.
- This architecturealso provides a mechanism for a level of abstraction for the operations performed by every stage, such as health metrics and alerting.
- Stagescome in three basic flavors: source stage, transformation stage, and sink stages.
- a source stagecontrols how a pipeline getting its data, including its source location, format, and similar conditions.
- a transformation stageperforms operations to manipulating the data received by the pipeline from a source stage.
- a sink stagescontrols where any resulting data is stored following its processing through a pipeline, which also includes its location, format, and similar conditions.
- environment stagesmay also be part of a pipeline. These stages define and manipulate operating conditions defined above as environmental conditions.
- a stage, such as stage 6 1822 , within pipeline 1800may itself be constructed using a workflow defined in exactly the same way.
- Dataenters stage 15 1831 as a data stream and exits as a data stream in which the number of processing steps implemented as a separately defined workflow pipeline used as modular stage element.
- Downstream stages, such as stage 7 1823does not know whether the data it receives is from a self-contained implementation of stage 6 1822 , or from an embedded workflow such as from stage 17 1833 .
- Hierarchical arrangements of workflows in such a mannerpermits construction of complex workflow from a combination of less complex workflows. All of this configuration of workflows may be defined in the declarative form described herein, and may use stages implemented in different backend processing engines such as Flink, Spark, Beam or similar data streaming processing technologies.
- workflow pipeline 1800utilizes a common data context permitting easy data exchange and integration of stages implemented in the various processing engines without complication.
- a common data exchange formatsuch as JSON
- datamay be specified using a common set of terms to permit ease of interoperability.
- a simple examplewould be to transform all incoming data streams into a standard set of values. For example, data such as distance, temperature, and time (zone) may be provided in various units. By transforming the data into a common set of units, all workflows may interoperate without issue. Data may be retransformed into a set of units useful by a user once the processing is otherwise completed.
- CEPComplex Event Processing
- FIG. 19is a diagram of a computing operating states 1900 for a pipeline according to one aspect of the present invention.
- a pipeline 1800and its component stages, will operate in one of a set of possible operating states.
- the pipelinebegins in an idle state 1901 once it has been loaded into computing resources. No data processing operations occur in this state.
- the pipelinenext enters a started state 1902 when the pipeline is launched. From here, the pipeline can transition to a running state 1903 to go to a stopped state 1906 . Data is processed through each of the stages in the pipeline while in the running state 1903 .
- the pipelinecan go to a paused state 1904 or a stopped state. In both cases, data processing is halted. From a paused state 1904 , the data processing may resume from its last point in the data by restarting the pipeline to return it to a running state 1903 .
- the pipelinemay enter a deleted state 1907 when its stages and computing components are removed from the computing resources.
- the pipelineenters an updated state 1905 either when changes are made to the existing graph defining the data flow within the pipeline or when a base docker image used to create the pipeline changes that requires changing to existing pipelines.
- the stopped pipelineis reconfigured in the update state to permit the new definition for the pipeline to operate on data when the pipeline returns to a running state 1903 from the update state 1905 .
- FIG. 21is a system diagram detailing the components of a Production Rule System (PRS), according to an embodiment.
- a client 2105 computerconnects to a production rule system (PRS) 2110, via a REST API 2111 over a network.
- PRSis a rule system which enables many different functionalities, including making external function calls to domain-specific oracles, providing for generalization of semantic and datastream processing rules and preventing rule creep when defining multiple transitivity properties, allowing for scalar value comparisons of data (comparing ages, distances, etc.), allowing for aggregation of facts and rules from different knowledge bases, graphs, or both, allowing for JSON conversion of rules to and from a GraphStack with a universally unique identifier (UUID), providing the ability to instantiate nodes with specified properties in a GraphStack, provide for a message queue through a Command-Line Interface (CLI) or Graphical User Interface (GUI), and rule building through an API, and allowing for new rules and modified rules to be
- a REST API 2111provides the forward-facing access to PRS 2110 functionality for a client 2105 , the PRS further comprising a set of core components 2120 which operate a further set of construction and evaluation protocols 2130 .
- the construction and evaluation protocolsinclude a data parser 2131 , data evaluator 2132 , and data constructor 2133 .
- the remaining core components 2120include at least an engine 2121 which drives the overall system and receives semantic data from a data construction component 2133 , forwarding processed data to a fact registry interface 2122 and an PRS client 2112 .
- a fact registry interface 2122may register new data selectively or automatically with a knowledge base 2140 which includes a directed knowledge graph and a multidimensional time series database (MDTSDB), and communicate registered data and the result of attempts to register new data with the PRS engine 2121 .
- An PRS engine 2121operates the construction and evaluation protocols 2130 to parse data sent through the REST API 2111 , and sends results and further queries for backend oracles 2150 to a PRS client 2112 .
- An PRS client 2112represents the PRS system 2110 communicating with backend oracles 2150 which in turn send the results of these modified queries to the client 2105 , thus completing the cycle and allowing the rule system 2110 to act as a modular, integrable front-end to other systems for semantic data and API call processing.
- the PRSmay declaratively specify windowed rules, wherein rules may be established for events occurring within a given time window. For example, a windowed rule may be established that counts the number of login attempts made within a two-minute time window. The window may be a “tumbled” or “sliding” window that repeatedly refreshes on a periodic basis to apply the rule to the time window just prior to the refresh.
- CEPComplex Event Processing
- Exactly-once semantics settingsmay be preserved according to some embodiments when registering a new fact or datapoint 2122 in a knowledge base 2140 , such that appearance of one semantically similar or identical datapoint in future processed data may achieve idempotency and cause an effect in the system only the first time it is encountered, but not subsequent times, such as when certain forms of machines have an “ON” and “OFF” switch respectively, wherein the “ON” switch does not perform any other actions after being pressed an initial time, until the device is turned “OFF.”
- an event datastreammay be processed with semantic learning and examination that contains reference to a temperature of 72 degrees in a specific geographical area.
- the idempotencymay mean that even after a change from the exactly-once occurrence, the occurrence will not trigger a new event.
- the oracles 2150may comprise any plurality or combination of services and technologies and components, which are utilized for database storage and data stream processing, which the PRS 2110 may communicate with to help with backend processing.
- a databasemay be included either in the oracles 2150 backend or in the knowledge base 2140 , or both, to support the integration of fixed-point rule semantics, providing for analysis of data and semantic data especially by comparison to a fixed point after refinement using machine learning.
- FIG. 22is a system diagram illustrating cyclic workflow stages in a pipeline of data analysis, according to an embodiment.
- a client 2210 systemsends a query or batch of data for processing to one of four possible workflow stages, either an environmental stage 2220 , a source stage 2230 , a transformation stage 2240 , or a sink stage 2250 . All workflow stages may feed into other workflow stages as shown by directional arrows, or at any point may forward the data from processing in the specified workflow stage back to the client for viewing, without forwarding to another workflow stage.
- an environmental data stage 2220is the only workflow stage capable of transmitting data as-needed between all three of the other workflow stages.
- An environmental workflow stageis utilized when environmental variables, settings, and initializations must be set, for instance initialization of other workflow stages, or of knowledge graph nodes, or other environmental attributes of interest.
- a source stage of workflow 2220is where data is analyzed to determine, broadly speaking, the origin and acquisition of the data, before either returning the result of the workflow immediately to the client 2210 or continuing to a transformation stage 2240 .
- a transformation stage of workflow 2240is where data may be manipulated, and represents such workflow steps and functionality as starting a data pipeline, shutting down a data pipeline, and editing a data pipeline for the flow and processing of data as required.
- This stage of the workflowmay return to the client 2210 or continue on to a data sink stage 2250 , which includes functions regarding where to put or send data after processing, or where to send data as received directly from a client 2210 .
- the workflow diagram as shownillustrates a cyclical nature wherein data and operations can be accomplished in one of several workflow stages, forwarded either to another workflow stage or returned back to the client, and repeated, until a client no longer desires to operate according to the defined workflow.
- a novel, declarative domain-specific languagemay be utilized in the workflow cycle.
- several functions of a novel DSLmay be utilized, including a capability for bidirectional dependencies on operations (for instance, “A->B” may be used to specify B depending on A before executing, or “B ⁇ -A” for the same), channel or domain-specific directional dependencies (for instance, “A->(“EXAMPLE”,B)” may be interpreted as B has a dependency on A's EXAMPLE signal, channel, or argument), multi-argument support (for instance, “A->(set(“EXAMPLE”,“EXAMPLE 2”),B)), and may be modular, for new language definitions and uses to be defined as needed.
- FIG. 24is a flow diagram illustrating an exemplary overview of a process for automated planning, according to a preferred embodiment.
- a distributed computational graph (DCG) 2410receives system observations from a system observation engine 2420 . These system state observations are used by the DCG to determine a set of execution instructions for a planning task, which are provided to an AP service 2320 for execution.
- AP service 2320retrieves an initial state 2430 and set of objectives 2440 from storage 2310 a - n , to form the boundary conditions for the work task (that is, what the system state looks like at the beginning of execution, and what it needs to look like when execution has completed).
- Additional inputmay also be received or collected from external sources 2450 , such as user input or online content retrieved through RESTful APIs.
- the execution instructionsare then broken up into discrete work tasks by master nodes within the AP service 2320 (as described above, with reference to FIG. 23 ) and work tasks are then assigned to worker nodes operating within the AP service (as described above in FIG. 23 ).
- the AP serviceproduces a set of execution plans and policies that are then provided to the DCG 2410 , which in turn produces a set of actions to be taken based on the plans and policies.
- a concrete action planis produced from state observations and machine learning through the use of DCG pipelines and parallel work processing using the AP service worker nodes, automating the planning process and producing a clear path to reach a desired end state.
- FIG. 25is a flow diagram illustrating an exemplary process for a single-run AP job.
- an AP service 2320when tasked with a set of execution instructions an AP service 2320 first validates all input data 2501 . This includes (but is not limited to) validating known initial information about the world and system (these describe environmental conditions and context in which the job is being run), objectives for the work job, available resources, constraints on job execution, and initial state expectation (that is, what the system anticipates the starting state to look like).
- AP service 2320then constructs a plurality of component models and a planning instance 2502 , which serve as a simulation model for developing work tasks within the context of the specific job.
- Work task executionthen comprises a multi-step operation 2510 utilizing worker nodes to process discrete tasks in parallel, beginning with seeding the newly-constructed planning instance with parameterized models 2511 ; in other words, the instance is populated with simulated models of various factors such as (for example, including but not limited to) environmental factors or job constraints as described above, based on the component models generated by the master nodes (which are provided to worker nodes as needed by their respective master nodes).
- Executionis then initialized 2512 , and master nodes assign individual work tasks to worker nodes for processing. Results of work task execution are collected and analyzed by master nodes 2513 , and any uncertainty estimation is performed 2514 as appropriate. The final results, including any uncertainty values to place the results in the proper context for decision making, are then provided to the requestor 2515 .
- FIG. 26is a block diagram illustrating an exemplary process for a multiple-run AP job.
- executiondoes not conclude immediately when a single AP processing job is complete; instead, additional runs are executed iteratively until a plurality of end conditions (such as a timer, a specified number of runs, or a specific desired outcome is generated) are met.
- a plurality of end conditionssuch as a timer, a specified number of runs, or a specific desired outcome is generated
- initial validation 2601a plurality of end conditions are specified 2602 .
- Initial parameters and planning instanceare then constructed 2603 , and execution of the simulation by worker nodes then proceeds 2510 .
- analysisnow includes determining if end conditions have been met 2604 .
- master nodesWhen the required end conditions are met, such as a specified number of runs, master nodes are instructed to stop assigning new work tasks to worker nodes; this effectively halts the execution once all pending tasks have completed, without needing to send multiple stop instructions or synchronize activity between nodes. If a worker node is not assigned work, it simply waits for the next task to be assigned; worker nodes need not have any awareness of the state of an overall AP job, and simply carry out individual tasks as they are assigned. When execution has concluded, results are published to the requestor 2605 .
- FIG. 10is a process flow diagram of a method 1000 for predictive analysis of very large data sets using the distributed computational graph.
- One or more streams of data from a plurality of sourceswhich includes, but is in no way not limited to, a number of physical sensors, web based questionnaires and surveys, monitoring of electronic infrastructure, crowd sourcing campaigns, and direct human interaction, may be received by system 1001 .
- the received streamis filtered 1002 to exclude data that has been corrupted, data that is incomplete or misconfigured and therefore unusable, data that may be intact but nonsensical within the context of the analyses being run, as well as a plurality of predetermined analysis related and unrelated criteria set by the authors.
- Filtered datamay be split into two identical streams at this point (second stream not depicted for simplicity), wherein one sub stream may be sent for batch processing 1600 while another sub stream may be formalized 1003 for transformation pipeline analysis 1004 , 561 , 600 , 700 , 800 , 900 .
- Data formalization for transformation pipeline analysisacts to reformat the stream data for optimal, reliable use during analysis. Reformatting might entail, but is not limited to: setting data field order, standardizing measurement units if choices are given, splitting complex information into multiple simpler fields, and stripping unwanted characters, again, just to name a few simple examples.
- the formalized data streammay be subjected to one or more transformations. Each transformation acts as a function on the data and may or may not change the data.
- transformations working on the same data stream where the output of one transformation acts as the input to the nextare represented as transformation pipelines. While the great majority of transformations in transformation pipelines receive a single stream of input, modify the data within the stream in some way and then pass the modified data as output to the next transformation in the pipeline, the invention does not require these characteristics.
- individual transformationscan receive input of expected form from more than one source 1300 or receive no input at all as would a transformation acting as a timestamp.
- individual transformationsmay not modify the data as would be encountered with a data store acting as a queue for downstream transformations 1303 , 1305 , 1405 , 1407 , 1505 .
- individual transformationsmay provide output to more than one downstream transformation 1400 .
- This abilitylends itself to simulations where multiple possible choices might be made at a single step of a procedure all of which need to be analyzed. While only a single, simple use case has been offered for each example, in each case, that example was chosen for simplicity of description from a plurality of possibilities, the examples given should not be considered to limit the invention to only simplistic applications.
- transformations in a transformation pipeline backbonemay form a linear, a quasi-linear arrangement or may be cyclical 1500 , where the output of one of the internal transformations serves as the input of one of its antecedents allowing recursive analysis to be run.
- the result of transformation pipeline analysismay then be modified by results from batch analysis 1005 of the data stream 1600 and output 1006 in format predesigned by the authors of the analysis with could be human readable summary printout, human readable instruction printout, human-readable raw printout, data store, or machine encoded information of any format that may be used in further automated analysis or action schema.
- FIG. 11is a process flow diagram of a method 1100 for an embodiment of modeling the transformation pipeline module 561 of the invention as a directed graph using graph theory.
- the individual transformations 1102 , 1104 , 1106 of the transformation pipeline t 1 . . . t nsuch that each t i T are represented as graph nodes. Transformations belonging to T are discrete transformations over individual datasets d i , consistent with classical functions. As such, each individual transformation t j , receives a set of inputs and produces a single output.
- FIG. 12is a process flow diagram of a method 1200 for one embodiment of a linear transformation pipeline 1201 .
- Thisis the simplest of configurations as the input stream is acted upon by the first transformation node 1202 and the remainder of the transformations within the pipeline are then performed sequentially 1202 , 1203 , 1204 , 1205 for the entire pipeline with no introduction of new data internal to the initial node or splitting output stream prior to last node of the pipeline 1205 .
- the result of the transformation pipelineis then sent back out to any message and output processes 1206 .
- This configurationis the current state of the art for transformation pipelines and is the most general form of these constructs.
- Linear transformation pipelinesrequire no special manipulation to simplify the data pathway and are thus referred to as non-decomposable.
- the example depicted in this diagramwas chosen to convey the configuration of a linear transformation pipeline and is the simplest form of the configuration felt to show the point. It in no way implies limitation of the invention.
- FIG. 13is a process flow diagram of a method 1300 for one embodiment of a transformation pipeline where one transformation node 1307 in a transformation pipeline receives data streams from two source transformation nodes 1301 .
- the inventionhandles this transformation pipeline configuration by decomposing or serializing the input events 1302 - 1303 , 1304 - 1305 heavily relying on post transformation function continuation.
- the results of individual transformation nodes 1302 , 1304just antecedent to the destination transformation node 1306 and placed into a single specialized data storage transformation node 1303 , 1305 (shown twice as process occurs twice).
- the combined resultsthen retrieved from the data store 1306 and serve as the input stream for the transformation node within the transformation pipeline backbone 1307 , 1308 .
- the example depicted in this diagramwas chosen to convey the configuration of transformation pipelines with individual transformation nodes that receive input from two source nodes 1302 , 1304 and is the simplest form of the configuration felt to show the point. It in no way implies limitation of the invention. Any number of permutations and topologies possible, especially as the invention places no design restrictions on the number of transformation nodes receiving input from greater than one sources or the number sources providing input to a destination node.
- FIG. 14is a process flow diagram of a method 1400 for one embodiment of a transformation pipeline where one transformation node 1402 sends output to a second node 1403 in a transformation pipeline, which then may send output data stream to two destination transformation nodes 1401 , 1406 , 1408 in potentially two separate transformation pipelines.
- the inventionhandles this transformation pipeline configuration by decomposing or serializing the output events 1404 , 1405 - 1406 , 1407 - 1408 .
- the results of the source transformation node 1403just antecedent to the destination transformation nodes 1406 and placed into a single specialized data storage transformation node 1404 , 1405 , 1407 (shown three times as storage occurs and retrieval occurs twice).
- the results of the antecedent transformation nodemay then be retrieved from a data store 1404 and serves as the input stream for the transformation nodes two downstream transformation pipeline 1406 , 1408 .
- the example depicted in this diagramwas chosen to convey the configuration of transformation pipelines with individual transformation nodes that send output streams to two destination nodes 1406 , 1408 and is the simplest form of the configuration felt to show the point. It in no way implies limitation of the invention. Any number of permutations and topologies possible, especially as the invention places no design restrictions on the number of transformation nodes sending output to greater than one destination or the number destinations receiving input from a source node.
- FIG. 15is a process flow diagram of a method 1500 for one embodiment of a transformation pipeline where the topology of all or part of the pipeline is cyclical 1501 .
- the output stream of one transformation node 1504acts as an input of an antecedent transformation node within the pipeline 1502 serialization or decomposition linearizes this cyclical configuration by completing the transformation of all of the nodes that make up a single cycle 1502 , 1503 , 1504 and then storing the result of that cycle in a data store 1505 . That result of a cycle is then reintroduced to the transformation pipeline as input to the first transformation node of the cycle 1506 .
- FIG. 16is a process flow diagram of a method 1600 for one embodiment of the batch data stream analysis pathway which forms part of the invention and allows streaming data to be interpreted with historic context.
- One or more streams of data from a plurality of sourceswhich includes, but is in no way not limited to, a number of physical sensors, web based questionnaires and surveys, monitoring of electronic infrastructure, crowd sourcing campaigns, and direct human interaction, is received by the system 1601 .
- the received streammay be filtered 1602 to exclude data that has been corrupted, data that is incomplete or misconfigured and therefore unusable, data that may be intact but nonsensical within the context of the analyses being run, as well as a plurality of predetermined analysis related and unrelated criteria set by the authors.
- Data formalization 1603 for batch analysisacts to reformat the stream data for optimal, reliable use during analysis. Reformatting might entail, but is not limited to: setting data field order, standardizing measurement units if choices are given, splitting complex information into multiple simpler fields, and stripping unwanted characters, again, just to name a few simple examples.
- the filtered and formalized streamis then added to a distributed data store 1604 due to the vast amount of information accrued over time.
- the inventionhas no dependency for specific data stores or data retrieval model.
- data stored in the batch pathway storecan be used to track changes in specifics of the data important to the ongoing analysis over time, repetitive data sets significant to the analysis or the occurrence of critical points of data 1605 .
- individual transformation nodes 620may be saved and can be edited also all nodes of a transformation pipeline 600 keep a summary or summarized view (analogous to a network routing table) of applicable parts of the overall route of the pipeline along with detailed information pertaining to adjacent two nodes.
- This framework informationenables steps to be taken and notifications to be passed if individual transformation nodes 640 within a transformation pipeline 600 become unresponsive during analysis operations.
- Combinations of results from the batch pathway, partial and streaming output results from the transformation pipeline, administrative directives from the authors of the analysis as well as operational status messages from components of the distributed computational graphare used to perform system sanity checks and retraining of one or more of the modules of the system 1606 . These corrections are designed to occur without administrative intervention under all but the most extreme of circumstances with deep learning capabilities present as part of the system manager and retrain module 563 responsible for this task.
- FIG. 20A-20Dis a process flow diagram for a set of processing operations used in a pipeline processing system according to one aspect of the present invention.
- the controlling system 1703communicates with and controls the operation of a pipeline using a set of API commands that include Post, Get, Delete, and Put commands.
- FIG. 20Aillustrates the operation of the Post 2001 commands.
- the Post commandsinclude an api/pipelines post and an api/pipelines/validate commands.
- the POST/api/pipelinesis a command having a content type: ‘application/json.’ This command is the entry point. It creates a new pipeline in the database of pipelines but does not start the pipeline. To start the pipeline, call ‘GET . . . /env’ and ‘GET . . . /data’ commands described below. Invalid pipelines may be saved at this point, future calls to this pipeline will be validated as part of the operation of the command.
- the commandhas the following payload fields: ‘pipeline;’ (required): ‘stageGraphBuilder’-a JSON representation of a valid pipeline; (required): ‘version’—the system version expected. An error will occur if the manager's version is different; (optional): ‘uuid’—If none is provided one will be created and returned in the response payload; (optional): ‘name’—A human readable name for the pipeline, uniqueness is not enforced; (optional): ‘description’—A description for end users; and (optional): ‘tags’—Keywords or terms associated with the pipeline (these tags are stored in an array).
- the POST/api/pipelines/validateis a command having a content type: ‘application/json.’ This command validates a pipeline. A pipeline with no environmental stages and no data processing stages is considered invalid.
- the commanduses payload fields: ‘pipeline’ (required): See [‘POST /api/pipelines’](#post-apipipelines).
- An example responseis:
- the commandis received 2021 and the pipeline is deserialized 2022 and a pipeline validation call is made 2023 .
- a response 200is returned with a list if invalid stages and paths are found 2024 .
- FIGS. 20B and 20Cillustrate the operation of a set of Get 2002 and Post commands.
- APACHE FLINKTMwill be used throughout the figures to refer to a data management backend.
- the GET/api/pipelines/ ⁇ uuid ⁇ commandis a command having a content type: ‘application/json.’
- the commandgets the pipeline previously posted pipeline from the database.
- the commandis received 2031 and data is obtained from the data store 2032 . If the pipeline exists 2033 in the data base, a pipeline definition is returned 2035 ; otherwise a reject 404 pipeline not found is returned 2034 .
- the POST/api/pipelines/ ⁇ uuid ⁇ /env/startis a command that calls the environmental setup for a pipeline.
- the commandis received 2041 and data is obtained from the data store 2042 . If the pipeline exists 2043 in the data base, a request accepted is returned 2044 ; otherwise a reject 404 pipeline not found is returned 2034 .
- the POST/api/pipelines/ ⁇ uuid ⁇ /env/status commandreturns the statuses of the environmental stages.
- the commandis received 2051 and data is obtained from the data store 2052 . If the pipeline exists 2053 in the data base, a stage ID and status is obtained 2054 and returned 2055 ; otherwise a reject 404 pipeline not found is returned 2034 .
- the POST/api/pipelines/ ⁇ uuid ⁇ /env/stop commandcalls the environmental teardown in a pipeline. If the data processing stages are still running when this endpoint is called, this endpoint returns an error. In other words, call ‘POST . . . /data/stop’ before calling this endpoint.
- the commandis received 2061 and data is obtained from the data store 2062 . If the pipeline exists 2063 in the data base, a request accepted is returned 2064 ; otherwise a reject 404 pipeline not found is returned 2034 .
- the commandis received 2071 and a test if an active pipeline exists 2072 in Flink. If the pipeline is not active, and already running rejection is returned 2100 ; otherwise a test to determine if the pipeline exists 2073 is performed. If the pipeline is not in the database a reject 404 pipeline not found is returned 2074 . If the pipeline exists in the database, the pipeline is deserialized 2075 . A test to determine if the operation was a success 2076 is performed and if not, a rejection ENV is not in a proper state is returned 2077 .
- a request to Flinkis made 2078 and a status of the request is tested 2079 a , If the status is good, the accepted work is returned 2079 b ; otherwise a Reject Flink rejects pipeline state is returned 2079 c.
- the POST/api/pipelines/ ⁇ uuid ⁇ /start/allstarts both the environmental and the data processing stages in a pipeline. Starts the pipeline from the most recent save point, if one exists.
- the commanduses payload parameters: ‘taskmanager-heap-mb’—the amount of heap to allocate to each task manger; ‘jobmanager-heap-mb’—the amount of heap to allocate to each job manager.
- the GET/api/pipelines/ ⁇ uuid ⁇ /data/status 2081returns the status of the data processing stages, by first determining if the pipeline exists in Flink 2082 , following up with a check for the pipeline in the database if the pipeline does not exist in Flink 2083 . If it does exist in the database, a “pipeline never started” status may be returned 2084 , while if the pipeline does not exist in the database, a “404 pipeline not found” 2074 error may be returned. If, however, the pipeline does exist in Flink 2082 , the Flink status of the pipeline is fetched 2085 and returned 2086 .
- the POST/api/pipelines/ ⁇ uuid ⁇ /data/stop commandstops the data processing stages in a pipeline (i.e., calls Flink with a save point). Returns an error if the pipeline does not have data processing stages.
- the commanduses request parameter ‘graceful’ (optional): indicates whether to stop the pipeline with a save point. Acceptable values: ‘true’, ‘false’ (defaults to ‘true’).
- the commanduses request parameter ‘graceful’ (optional): indicates whether to stop the pipeline with a save point. Acceptable values: ‘true’, ‘false’ (defaults to ‘true’).
- commandis received 2091 and a test 2092 determines if the pipeline exists in Flink. If is exists, a request to Flink 2095 is made and the Flink results are returned 2096 ; otherwise a reject Pipeline ID is not running is returned 2094 .
- FIG. 20DIllustrate the operation of Delete 2003 and Put commands.
- the DELETE /api/pipelines/ ⁇ uuid ⁇ 2003deletes the pipeline from the database. Does not stop the pipeline, so it's expected that the user calls ‘GET . . . /stop’ first. Calls to delete the pipeline while it is already active will be rejected.
- the commandis received 2301 and data is retrieved from the data store 2302 .
- Test 2303determines if the pipeline exists in the database. If not a Reject 404 pipeline not found is returned; otherwise test 2305 determines if the pipeline is active in Flink. If not, a Reject 404 pipeline not found is also returned 2304 ; otherwise the pipeline is deserialized 2306 and a stop Env function is called 2307 .
- Test 2308determines whether the teardown was successful. If so, an update to the pipeline is made to indicate a new state 2309 ; otherwise remediation may be initiated 2310 .
- the PUT/api/pipelines command 2004is a command having a content type: ‘application/json.’ This command updates the pipeline in the database, but does not start or stop the pipeline. A pipeline with no environmental stages and no data processing stages is considered invalid.
- the commanduses payload fields: ‘pipeline’ (required): See [‘POST/api/pipelines’](#post-apipipelines) uuid of pipeline to update must be in the payload.
- Test 2403determines if the pipeline exists in the database. If not a Reject 404 pipeline not found is returned 2404 ; otherwise test 2405 determines if the ENV has not been started. If it has not been started, a Reject cannot update pipeline not active is returned 2408 ; otherwise the pipeline is inserted into the database 2406 and a success indication is returned 2407 .
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Abstract
Description
Application No. Date Filed Title Current Herewith DISTRIBUTED AUTOMATED PLANNING application AND EXECUTION PLATFORM FOR DESIGNING AND RUNNING COMPLEX PROCESSES Is a continuation-in-part of: 17/061,195 Oct. 1, 2020 CROWDSOURCED INNOVATION LABORATORY AND PROCESS IMPLENTATION SYSTEM which is a continuation-in-part of: 17/035,029 Sep. 28, 2020 SYSTEM AND METHOD FOR CREATION AND IMPLEMENTATION OF DATA PROCESSING WORKFLOWS USING A DISTRIBUTED COMPUTATIONAL GRAPH which is a continuation-in-part of: 17/008,276 Aug. 31, 2020 PRIVILEGE ASSURANCE OF ENTERPRISE COMPUTER NETWORK ENVIRONMENTS which is a continuation-in-part of: 17/000,504 Aug. 24, 2020 ADVANCED DETECTION OF IDENTITY- BASED ATTACKS TO ASSURE IDENTITY FIDELITY IN INFORMATION TECHNOLOGY ENVIRONMENTS which is a continuation-in-part of: 16/855,724 Apr. 22, 2020 ADVANCED CYBERSECURITY THREAT MITIGATION USING SOFTWARE SUPPLY CHAIN ANALYSIS which is a continuation-in-part of: 16/836,717 Mar. 31, 2020 HOLISTIC COMPUTER SYSTEM CYBERSECURITY EVALUATION AND SCORING which is a continuation-in-part of: 15/887,496 Feb. 2, 2018 SYSTEM AND METHODS FOR Patent Issue Date SANDBOXED MALWARE ANALYSIS AND 10,783,241 Sep. 22, 2020 AUTOMATED PATCH DEVELOPMENT, DEPLOYMENT AND VALIDATION which is a continuation-in-part of: 15/818,733 Nov. 20, 2017 SYSTEM AND METHOD FOR Patent Issue Date CYBERSECURITY ANALYSIS AND SCORE 10,673,887 Jun. 2, 2020 GENERATION FOR INSURANCE PURPOSES which is a continuation-in-part of: 15/725,274 Oct. 4, 2017 APPLICATION OF ADVANCED Patent Issue Date CYBERSECURITY THREAT MITIGATION 10,609,079 Mar. 31, 2020 TO ROGUE DEVICES, PRIVILEGE ESCALATION, AND RISK-BASED VULNERABILITY AND PATCH MANAGEMENT which is a continuation-in-part of: 15/655,113 Jul. 20, 2017 ADVANCED CYBERSECURITY THREAT Patent Issue Date MITIGATION USING BEHAVIORAL AND 10,735,456 Aug. 4, 2020 DEEP ANALYTICS which is a continuation-in-part of: 15/616,427 Jun. 7, 2017 RAPID PREDICTIVE ANALYSIS OF VERY LARGE DATA SETS USING AN ACTOR- DRIVEN DISTRIBUTED COMPUTATIONAL GRAPH and is also a continuation-in-part of: 15/237,625 Aug. 15, 2016 DETECTION MITIGATION AND Patent Issue Date REMEDIATION OF CYBERATTACKS 10,248,910 Apr. 2, 2019 EMPLOYING AN ADVANCED CYBER- DECISION PLATFORM which is a continuation-in-part of: 15/206,195 Jul. 8, 2016 ACCURATE AND DETAILED MODELING OF SYSTEMS WITH LARGE COMPLEX DATASETS USING A DISTRIBUTED SIMULATION ENGINE which is a continuation-in-part of: 15/186,453 Jun. 18, 2016 SYSTEM FOR AUTOMATED CAPTURE AND ANALYSIS OF BUSINESS INFORMATION FOR RELIABLE BUSINESS VENTURE OUTCOME PREDICTION which is a continuation-in-part of: 15/166,158 May 26, 2016 SYSTEM FOR AUTOMATED CAPTURE AND ANALYSIS OF BUSINESS INFORMATION FOR SECURITY AND CLIENT-FACING INFRASTRUCTURE RELIABILITY which is a continuation-in-part of: 15/141,752 Apr. 28, 2016 SYSTEM FOR FULLY INTEGRATED CAPTURE, AND ANALYSIS OF BUSINESS INFORMATION RESULTING IN PREDICTIVE DECISION MAKING AND SIMULATION which is a continuation-in-part of: 15/091,563 Apr. 5, 2016 SYSTEM FOR CAPTURE, ANALYSIS AND Patent Issue Date STORAGE OF TIME SERIES DATA FROM 10,204,147 Feb. 12, 2019 SENSORS WITH HETEROGENEOUS REPORT INTERVAL PROFILES and is also a continuation-in-part of: 14/986,536 Dec. 31, 2015 DISTRIBUTED SYSTEM FOR LARGE Patent Issue Date VOLUME DEEP WEB DATA 10,210,255 Feb. 19, 2019 EXTRACTION and is also a continuation-in-part of: 14/925,974 Oct. 28, 2015 RAPID PREDICTIVE ANALYSIS OF VERY LARGE DATA SETS USING THE DISTRIBUTED COMPUTATIONAL GRAPH Current Herewith DISTRIBUTED AUTOMATED PLANNING application AND EXECUTION PLATFORM FOR DESIGNING AND RUNNING COMPLEX PROCESSES Is a continuation-in-part of: 17/061,195 Oct. 1, 2020 CROWDSOURCED INNOVATION LABORATORY AND PROCESS IMPLENTATION SYSTEM which is a continuation-in-part of: 17/035,029 Sep. 28, 2020 SYSTEM AND METHOD FOR CREATION AND IMPLEMENTATION OF DATA PROCESSING WORKFLOWS USING A DISTRIBUTED COMPUTATIONAL GRAPH which is a continuation-in-part of: 17/008,276 Aug. 31, 2020 PRIVILEGE ASSURANCE OF ENTERPRISE COMPUTER NETWORK ENVIRONMENTS which is a continuation-in-part of: 17/000,504 Aug. 24, 2020 ADVANCED CYBERSECURITY THREAT MITIGATION USING SOFTWARE SUPPLY CHAIN ANALYSIS which is a continuation-in-part of: 16/855,724 Apr. 22, 2020 ADVANCED CYBERSECURITY THREAT MITIGATION USING SOFTWARE SUPPLY CHAIN ANALYSIS which is a continuation-in-part of: 16/836,717 Mar. 31, 2020 HOLISTIC COMPUTER SYSTEM CYBERSECURITY EVALUATION AND SCORING which is a continuation-in-part of: 15/887,496 Feb. 2, 2018 SYSTEM AND METHODS FOR Patent Issue Date SANDBOXED MALWARE ANALYSIS AND 10,783,241 Sep. 22, 2020 AUTOMATED PATCH DEVELOPMENT, DEPLOYMENT AND VALIDATION which is a continuation-in-part of: 15/823,285 Nov. 27, 2017 META-INDEXING, SEARCH, Patent Issue Date COMPLIANCE, AND TEST FRAMEWORK 10,740,096 Aug. 11, 2020 FOR SOFTWARE DEVELOPMENT which is a continuation-in-part of: 15/788,718 Oct. 19, 2017 DATA MONETIZATION AND EXCHANGE PLATFORM which claims priority, and benefit to: 62/568,307 Oct. 4, 2017 DATA MONETIZATION AND EXCHANGE PLATFORM and is also a continuation-in-part of: 15/788,002 Oct. 19, 2017 ALGORITHM MONETIZATION AND EXCHANGE PLATFORM which claims priority, and benefit to: 62/568,305 Oct. 4, 2017 ALGORITHM MONETIZATION AND EXCHANGE PLATFORM and is also a continuation-in-part of: 15/787,601 Oct. 18, 2017 METHOD AND APPARATUS FOR CROWDSOURCED DATA GATHERING, EXTRACTION, AND COMPENSATION which claims priority, and benefit to: 62/568,312 Oct. 4, 2017 METHOD AND APPARATUS FOR CROWDSOURCED DATA GATHERING, EXTRACTION, AND COMPENSATION and is also a continuation-in-part of: 15/616,427 Jun. 7, 2017 RAPID PREDICTIVE ANALYSIS OF VERY LARGE DATA SETS USING AN ACTOR- DRIVEN DISTRIBUTED COMPUTATIONAL GRAPH which is a continuation-in-part of: 14/925,974 Oct. 28, 2015 RAPID PREDICTIVE ANALYSIS OF VERY LARGE DATA SETS USING THE DISTRIBUTED COMPUTATIONAL GRAPHY Current Herewith DISTRIBUTED AUTOMATED PLANNING application AND EXECUTION PLATFORM FOR DESIGNING AND RUNNING COMPLEX PROCESSES Is a continuation-in-part of: 17/061,195 Oct. 1, 2020 CROWDSOURCED INNOVATION LABORATORY AND PROCESS IMPLENTATION SYSTEM which is a continuation-in-part of: 17/035,029 Sep. 28, 2020 SYSTEM AND METHOD FOR CREATION AND IMPLEMENTATION OF DATA PROCESSING WORKFLOWS USING A DISTRIBUTED COMPUTATIONAL GRAPH which is a continuation-in-part of: 17/008,276 Aug. 31, 2020 PRIVILEGE ASSURANCE OF ENTERPRISE COMPUTER NETWORK ENVIRONMENTS which is a continuation-in-part of: 17/000,504 Aug. 24, 2020 ADVANCED DETECTION OF IDENTITY- BASED ATTACKS TO ASSURE IDENTITY FIDELITY IN INFORMATION TECHNOLOGY ENVIRONMENTS which is a continuation-in-part of: 16/855,724 Apr. 22, 2020 ADVANCED CYBERSECURITY THREAT MITIGATION USING SOFTWARE SUPPLY CHAIN ANALYSIS which is a continuation-in-part of: 16/777,270 Jan. 30, 2020 CYBERSECURITY PROFILING AND RATING USING ACTIVE AND PASSIVE EXTERNAL RECONNAISSANCE which is a continuation-in-part of: 16/720,383 Dec. 19, 2019 RATING ORGANIZATION CYBERSECURITY USING ACTIVE AND PASSIVE EXTERNAL RECONNAISSANCE which is a continuation of: 15/823,363 Nov. 27, 2017 RATING ORGANIZATION Patent Issue Date CYBERSECURITY USING ACTIVE AND 10,560,483 Feb. 11, 2020 PASSIVE EXTERNAL RECONNAISSANCE which is a continuation-in-part of: 15/725,274 Oct. 4, 2017 APPLICATION OF ADVANCED Patent Issue Date CYBERSECURITY THREAT MITIGATION 10,609,079 Mar. 31, 2020 TO ROGUE DEVICES, PRIVILEGE ESCALATION, AND RISK-BASED VULNERABILITY AND PATCH MANAGEMENT Current Herewith DISTRIBUTED AUTOMATED PLANNING application AND EXECUTION PLATFORM FOR DESIGNING AND RUNNING COMPLEX PROCESSES Is a continuation-in-part of: 17/061,195 Oct. 1, 2020 CROWDSOURCED INNOVATION LABORATORY AND PROCESS IMPLENTATION SYSTEM which is a continuation-in-part of: 17/035,029 Sep. 28, 2020 SYSTEM AND METHOD FOR CREATION AND IMPLEMENTATION OF DATA PROCESSING WORKFLOWS USING A DISTRIBUTED COMPUTATIONAL GRAPH which is a continuation-in-part of: 17/008,276 Aug. 31, 2020 PRIVILEGE ASSURANCE OF ENTERPRISE COMPUTER NETWORK ENVIRONMENTS which is a continuation-in-part of: 17/000,504 Aug. 24, 2020 ADVANCED DETECTION OF IDENTITY- BASED ATTACKS TO ASSURE IDENTITY FIDELITY IN INFORMATION TECHNOLOGY ENVIRONMENTS which is a continuation-in-part of: 16/412,340 May 14, 2019 SECURE POLICY-CONTROLLED PROCESSING AND AUDITING ON REGULATED DATA SETS which is a continuation-in-part of: 16/267,893 Feb. 5, 2019 SYSTEM AND METHODS FOR DETECTING AND CHARACTERIZING ELECTROMAGNETIC EMISSIONS which is a continuation-in-part of: 16/248,133 Jan. 15, 2019 SYSTEM AND METHOD FOR MULTI- MODEL GENERATIVE SIMULATION MODELING OF COMPLEX ADAPTIVE SYSTEMS which is a continuation-in-part of: 15/813,097 Nov. 14, 2017 EPISTEMIC UNCERTAINTY REDUCTION USING SIMULATIONS, MODELS AND DATA EXCHANGE which is a continuation-in-part of: 15/616,427 Jun. 7, 2017 RAPID PREDICTIVE ANALYSIS OF VERY LARGE DATA SETS USING AN ACTOR- DRIVEN DISTRIBUTED COMPUTATIONAL GRAPH Current Herewith DISTRIBUTED AUTOMATED PLANNING application AND EXECUTION PLATFORM FOR DESIGNING AND RUNNING COMPLEX PROCESSES Is a continuation-in-part of: 17/061,195 Oct. 1, 2020 CROWDSOURCED INNOVATION LABORATORY AND PROCESS IMPLENTATION SYSTEM which is a continuation-in-part of: 17/035,029 Sep. 28, 2020 SYSTEM AND METHOD FOR CREATION AND IMPLEMENTATION OF DATA PROCESSING WORKFLOWS USING A DISTRIBUTED COMPUTATIONAL GRAPH which is a continuation-in-part of: 17/008,276 Aug. 31, 2020 PRIVILEGE ASSURANCE OF ENTERPRISE COMPUTER NETWORK ENVIRONMENTS which is a continuation-in-part of: 17/000,504 Aug. 24, 2020 ADVANCED DETECTION OF IDENTITY- BASED ATTACKS TO ASSURE IDENTITY FIDELITY IN INFORMATION TECHNOLOGY ENVIRONMENTS which is a continuation-in-part of: 16/412,340 May 14, 2019 SECURE POLICY-CONTROLLED PROCESSING AND AUDITING ON REGULATED DATA SETS which is a continuation-in-part of: 16/267,893 Feb. 5, 2019 SYSTEM AND METHODS FOR DETECTING AND CHARACTERIZING ELECTROMAGNETIC EMISSIONS which is a continuation-in-part of: 16/248,133 Jan. 15, 2019 SYSTEM AND METHOD FOR MULTI- MODEL GENERATIVE SIMULATION MODELING OF COMPLEX ADAPTIVE SYSTEMS which is also a continuation-in-part of: 15/806,697 Nov. 8, 2017 MODELING MULTI-PERIL CATASTROPHE USING A DISTRIBUTED SIMULATION ENGINE which is a continuation-in-part of: 15/376,657 Dec. 13, 2016 QUANTIFICATION FOR INVESTMENT Patent Issue Date VEHICLE MANAGEMENT EMPLOYING 10,402,906 Sep. 3, 2019 AN ADVANCED DECISION PLATFORM which is a continuation-in-part of: 15/237,625 Aug. 15, 2016 DETECTION MITIGATION AND Patent Issue Date REMEDIATION OF CYBERATTACKS 10,248,910 Apr. 2, 2019 EMPLOYING AN ADVANCED CYBER- DECISION PLATFORM Current Herewith DISTRIBUTED AUTOMATED PLANNING application AND EXECUTION PLATFORM FOR DESIGNING AND RUNNING COMPLEX PROCESSES Is a continuation-in-part of: 17/061,195 Oct. 1, 2020 CROWDSOURCED INNOVATION LABORATORY AND PROCESS IMPLENTATION SYSTEM which is a continuation-in-part of: 17/035,029 Sep. 28, 2020 SYSTEM AND METHOD FOR CREATION AND IMPLEMENTATION OF DATA PROCESSING WORKFLOWS USING A DISTRIBUTED COMPUTATIONAL GRAPH which is a continuation-in-part of 17/008,276 Aug. 31, 2020 PRIVILEGE ASSURANCE OF ENTERPRISE COMPUTER NETWORK ENVIRONMENTS which is a continuation-in-part of: 17/000,504 Aug. 24, 2020 ADVANCED DETECTION OF IDENTITY- BASED ATTACKS TO ASSURE IDENTITY FIDELITY IN INFORMATION TECHNOLOGY ENVIRONMENTS which is a continuation-in-part of: 16/412,340 May 14, 2019 SECURE POLICY-CONTROLLED PROCESSING AND AUDITING ON REGULATED DATA SETS which is a continuation-in-part of: 16/267,893 Feb. 5, 2019 SYSTEM AND METHODS FOR DETECTING AND CHARACTERIZING ELECTROMAGNETIC EMISSIONS which is a continuation-in-part of: 16/248,133 Jan. 15, 2019 SYSTEM AND METHOD FOR MULTI- MODEL GENERATIVE SIMULATION MODELING OF COMPLEX ADAPTIVE SYSTEMS which is a continuation-in-part of: 15/806,697 Nov. 8, 2017 MODELING MULTI-PERIL CATASTROPHE USING A DISTRIBUTED SIMULATION ENGINE which is a continuation-in-part of: 15/343,209 Nov. 4, 2016 RISK QUANTIFICATION FOR INSURANCE PROCESS MANAGEMENT EMPLOYING AN ADVANCED DECISION PLATFORM which is a continuation-in-part of: 15/237,625 Aug. 15, 2016 DETECTION MITIGATION AND Patent Issue Date REMEDIATION OF CYBERATTACKS 10,248,910 Apr. 2, 2019 EMPLOYING AN ADVANCED CYBER- DECISION PLATFORM and is also a continuation-in-part of: 15/229,476 Aug. 5, 2016 HIGHLY SCALABLE DISTRIBUTED Patent Issue Date CONNECTION INTERFACE FOR DATA 10,454,791 Oct. 22, 2019 CAPTURE FROM MULTIPLE NETWORK SERVICE SOURCES which is a continuation-in-part of: 15/206,195 Jul. 8, 2016 ACCURATE AND DETAILED MODELING OF SYSTEMS WITH LARGE COMPLEX DATASETS USING A DISTRIBUTED SIMULATION ENGINE Current Herewith DISTRIBUTED AUTOMATED PLANNING application AND EXECUTION PLATFORM FOR DESIGNING AND RUNNING COMPLEX PROCESSES Is a continuation-in-part of: 17/061,195 Oct. 1, 2020 CROWDSOURCED INNOVATION LABORATORY AND PROCESS IMPLENTATION SYSTEM which is a continuation-in-part of: 17/035,029 Sep. 28, 2020 SYSTEM AND METHOD FOR CREATION AND IMPLEMENTATION OF DATA PROCESSING WORKFLOWS USING A DISTRIBUTED COMPUTATIONAL GRAPH which is a continuation-in-part of: 17/008,276 Aug. 31, 2020 PRIVILEGE ASSURANCE OF ENTERPRISE COMPUTER NETWORK ENVIRONMENTS which is a continuation-in-part of: 17/000,504 Aug. 24, 2020 ADVANCED DETECTION OF IDENTITY- BASED ATTACKS TO ASSURE IDENTITY FIDELITY IN INFORMATION TECHNOLOGY ENVIRONMENTS which is a continuation-in-part of: 16/412,340 May 14, 2019 SECURE POLICY-CONTROLLED PROCESSING AND AUDITING ON REGULATED DATA SETS which is a continuation-in-part of: 16/267,893 Feb. 5, 2019 SYSTEM AND METHODS FOR DETECTING AND CHARACTERIZING ELECTROMAGNETIC EMISSIONS which is a continuation-in-part of: 16/248,133 Jan. 15, 2019 SYSTEM AND METHOD FOR MULTI- MODEL GENERATIVE SIMULATION MODELING OF COMPLEX ADAPTIVE SYSTEMS which is a continuation-in-part of: 15/673,368 Aug. 9, 2017 AUTOMATED SELECTION AND PROCESSING OF FINANCIAL MODELS which is a continuation-in-part of: 15/376,657 Dec. 13, 2016 QUANTIFICATION FOR INVESTMENT Patent Issue Date VEHICLE MANAGEMENT EMPLOYING 10,402,906 Sep. 3, 2019 AN ADVANCED DECISION PLATFORM Current Herewith DISTRIBUTED AUTOMATED PLANNING application AND EXECUTION PLATFORM FOR DESIGNING AND RUNNING COMPLEX PROCESSES Is a continuation-in-part of: 17/061,195 Oct. 1, 2020 CROWDSOURCED INNOVATION LABORATORY AND PROCESS IMPLENTATION SYSTEM which is a continuation-in-part of: 17/035,029 Sep. 28, 2020 SYSTEM AND METHOD FOR CREATION AND IMPLEMENTATION OF DATA PROCESSING WORKFLOWS USING A DISTRIBUTED COMPUTATIONAL GRAPH which is a continuation-in-part of: 17/008,276 Aug. 31, 2020 PRIVILEGE ASSURANCE OF ENTERPRISE COMPUTER NETWORK ENVIRONMENTS which is a continuation-in-part of: 17/000,504 Aug. 24, 2020 ADVANCED DETECTION OF IDENTITY- BASED ATTACKS TO ASSURE IDENTITY FIDELITY IN INFORMATION TECHNOLOGY ENVIRONMENTS which is a continuation-in-part of: 16/412,340 May 14, 2019 SECURE POLICY-CONTROLLED PROCESSING AND AUDITING ON REGULATED DATA SETS which is a continuation-in-part of: 16/267,893 Feb. 5, 2019 SYSTEM AND METHODS FOR DETECTING AND CHARACTERIZING ELECTROMAGNETIC EMISSIONS which is a continuation-in-part of: 16/248,133 Jan. 15, 2019 SYSTEM AND METHOD FOR MULTI- MODEL GENERATIVE SIMULATION MODELING OF COMPLEX ADAPTIVE SYSTEMS which is a continuation-in-part of: 15/849,901 Dec. 21, 2017 SYSTEM AND METHOD FOR OPTIMIZATION AND LOAD BALANCING OF COMPUTER CLUSTERS which is a continuation-in-part of: 15/835,312 Dec. 7, 2017 SYSTEM AND METHODS FOR MULTI- LANGUAGE ABSTRACT MODEL CREATION FOR DIGITAL ENVIRONMENT SIMULATIONS which is a continuation-in-part of: 15/186,453 Jun. 18, 2016 SYSTEM FOR AUTOMATED CAPTURE AND ANALYSIS OF BUSINESS INFORMATION FOR RELIABLE BUSINESS VENTURE OUTCOME PREDICTION Current Herewith DISTRIBUTED AUTOMATED PLANNING application AND EXECUTION PLATFORM FOR DESIGNING AND RUNNING COMPLEX PROCESSES Is a continuation-in-part of: 17/061,195 Oct. 1, 2020 CROWDSOURCED INNOVATION LABORATORY AND PROCESS IMPLENTATION SYSTEM which is a continuation-in-part of: 17/035,029 Sep. 28, 2020 SYSTEM AND METHOD FOR CREATION AND IMPLEMENTATION OF DATA PROCESSING WORKFLOWS USING A DISTRIBUTED COMPUTATIONAL GRAPH which is a continuation-in-part of: 17/008,276 Aug. 31, 2020 PRIVILEGE ASSURANCE OF ENTERPRISE COMPUTER NETWORK ENVIRONMENTS which is a continuation-in-part of: 17/000,504 Aug. 24, 2020 ADVANCED DETECTION OF IDENTITY- BASED ATTACKS TO ASSURE IDENTITY FIDELITY IN INFORMATION TECHNOLOGY ENVIRONMENTS which is a continuation-in-part of: 16/412,340 May 14, 2019 SECURE POLICY-CONTROLLED PROCESSING AND AUDITING ON REGULATED DATA SETS which is a continuation-in-part of: 16/267,893 Feb. 5, 2019 SYSTEM AND METHODS FOR DETECTING AND CHARACTERIZING ELECTROMAGNETIC EMISSIONS which is a continuation-in-part of: 16/248,133 Jan. 15, 2019 SYSTEM AND METHOD FOR MULTI- MODEL GENERATIVE SIMULATION MODELING OF COMPLEX ADAPTIVE SYSTEMS which is a continuation-in-part of: 15/849,901 Dec. 21, 2017 SYSTEM AND METHOD FOR OPTIMIZATION AND LOAD BALANCING OF COMPUTER CLUSTERS which is a continuation-in-part of: 15/835,436 Dec. 7, 2017 TRANSFER LEARNING AND DOMAIN Patent Issue Date ADAPTATION USING DISTRIBUTABLE 10,572,828 Feb. 25, 2020 DATA MODELS which is a continuation-in-part of: 15/790,457 Oct. 23, 2017 DISTRIBUTABLE MODEL WITH BIASES CONTAINED WITHIN DISTRIBUTED DATA which claims benefit of, and priority to: 62/568,298 Oct. 4, 2017 DISTRIBUTABLE MODEL WITH BIASES CONTAINED IN DISTRIBUTED DATA and is also a continuation-in-part of: 15/790,327 Oct. 23, 2017 DISTRIBUTABLE MODEL WITH DISTRIBUTED DATA which claims benefit of, and priority to: 62/568,291 Oct. 4, 2017 DISTRIBUTABLE MODEL WITH DISTRIBUTED DATA and is also a continuation-in-part of: 15/616,427 Jun. 7, 2017 RAPID PREDICTIVE ANALYSIS OF VERY LARGE DATA SETS USING AN ACTOR- DRIVEN DISTRIBUTED COMPUTATIONAL GRAPH and is also a continuation-in-part of: 15/141,752 Apr. 28, 2016 SYSTEM FOR FULLY INTEGRATED CAPTURE, AND ANALYSIS OF BUSINESS INFORMATION RESULTING IN PREDICTIVE DECISION MAKING AND SIMULATION Current Herewith DISTRIBUTED AUTOMATED PLANNING application AND EXECUTION PLATFORM FOR DESIGNING AND RUNNING COMPLEX PROCESSES Is a continuation-in-part of: 17/061,195 Oct. 1, 2020 CROWDSOURCED INNOVATION LABORATORY AND PROCESS IMPLENTATION SYSTEM which is a continuation-in-part of: 15/879,801 Jan. 25, 2018 PLATFORM FOR MANAGEMENT AND TRACKING OF COLLABORATIVE PROJECTS which is a continuation-in-part of: 15/379,899 Dec. 15, 2016 INCLUSION OF TIME SERIES GEOSPATIAL MARKERS IN ANALYSES EMPLOYING AN ADVANCED CYBER- DECISION PLATFORM which is a continuation-in-part of: 15/376,657 Dec. 13, 2016 QUANTIFICATION FOR INVESTMENT Patent Issue Date VEHICLE MANAGEMENT EMPLOYING N 10,402,906 Sep. 3, 2019 ADVANCED DECISION PLATFORM which is a continuation-in-part of: 15/237,625 Aug. 15, 2016 DETECTION MITIGATION AND Patent Issue Date REMEDIATION OF CYBERATTACKS 10,248,910 Apr. 2, 2019 EMPLOYING AN ADVANCED CYBER- DECISION PLATFORM which is a continuation-in-part of: 15/206,195 Jul. 8, 2016 ACCURATE AND DETAILED MODELING OF SYSTEMS WITH LARGE COMPLEX DATASETS USING A DISTRIBUTED SIMULATION ENGINE which is a continuation-in-part of: 15/186,453 Jun. 18, 2016 SYSTEM FOR AUTOMATED CAPTURE AND ANALYSIS OF BUSINESS INFORMATION FOR RELIABLE BUSINESS VENTURE OUTCOME PREDICTION which is a continuation-in-part of: 15/166,158 May 26, 2016 SYSTEM FOR AUTOMATED CAPTURE AND ANALYSIS OF BUSINESS INFORMATION FOR SECURITY AND CLIENT-FACING INFRASTRUCTURE RELIABILITY which is a continuation-in-part of: 15/141,752 Apr. 28, 2016 SYSTEM FOR FULLY INTEGRATED CAPTURE, AND ANALYSIS OF BUSINESS INFORMATION RESULTING IN PREDICTIVE DECISION MAKING AND SIMULATION which is a continuation-in-part of: 15/091,563 Apr. 5, 2016 SYSTEM FOR CAPTURE, ANALYSIS AND Patent Issue Date STORAGE OF TIME SERIES DATA FROM 10,204,147 Feb. 12, 2019 SENSORS WITH HETEROGENEOUS REPORT INTERVAL PROFILES and is also a continuation-in-part of: 14/986,536 Dec. 31, 2015 DISTRIBUTED SYSTEM FOR LARGE Patent Issue Date VOLUME DEEP WEB DATA 10,210,255 Feb. 19, 2019 EXTRACTION and is also a continuation-in-part of: 14/925,974 Oct. 28, 2015 RAPID PREDICTIVE ANALYSIS OF VERY LARGE DATA SETS USING THE DISTRIBUTED COMPUTATIONAL GRAPH Current Herewith DISTRIBUTED AUTOMATED PLANNING application AND EXECUTION PLATFORM FOR DESIGNING AND RUNNING COMPLEX PROCESSES Is a continuation-in-part of: 16/709,598 Dec. 10, 2019 RAPID PREDICTIVE ANALYSIS OF VERY LARGE DATA SETS USING THE DISTRIBUTED COMPUTATIONAL GRAPH USING CONFIGURABLE ARRANGEMENT OF PROCESSING COMPONENTS which is a continuation-in-part of: 14/925,974 Oct. 28, 2015 RAPID PREDICTIVE ANALYSIS OF VERY LARGE DATA SETS USING THE DISTRIBUTED COMPUTATIONAL GRAPH the entire specification of each of which is incorporated herein by reference.
the entire specification of each of which is incorporated herein by reference.- The disclosure relates to the field of automated tracking and management of collaborative projects.
- Automated planning is a branch of machine learning that focuses on computationally creating ordered sets of actions to perform a given task. Artificial intelligence has been used in this way for decades to plan robotic activity, controlling unmanned vehicles, and to plan manned operations such as space missions. However, current systems do not scale well when the number of objects in a given problem space is large. Current systems attempt to deal with this limitation using domain-independent heuristics, however this is an inelegant solution that results in large search trees that cannot be processed in a reasonable time and thus imposing a de facto limit on the size of the problem a given system is capable of handling.
- What is needed is a system that will allow users to design, instantiate, and run complex and evolving processes using a distributed, scalable system that can handle increasingly-complex problems through a distributed architecture and distributed computational graph-based data transformation pipelines.
- Accordingly, the inventor has developed and reduced to practice, a distributed automated planning and execution platform for designing, instantiating, and running complex and evolving processes. In a typical embodiment, a platform may be deployed in a distributed or federated architecture, without the need for strict synchronization across system components and instead relying on an “eventual agreement” model wherein consistency is achieved in an asynchronous, yet certain, manner. The system may also employ various machine learning techniques and simulations to continually improve and evolve through the use of data transformation pipelines, ensuring the system can scale as needed to handle increasingly complex processes.
- According to a preferred embodiment, a system for management and tracking of collaborative projects, comprising: an automated planning service comprising a memory, a processor, and a plurality of programming instructions stored in the memory thereof and operable on the processor thereof, wherein the programmable instructions, when operating on the processor, cause the processor to: operate a plurality of master nodes, each master node in turn operating a plurality of worker nodes; receive a plurality of simulation conditions at a master node; construct a plurality of simulation components based on the received simulation conditions; construct a planning model based on the received simulation conditions; assign, using a master node, a plurality of discrete simulation tasks to a plurality of worker nodes, each of the plurality of discrete simulation tasks being based on the constructed simulation components and planning model, wherein each of the plurality of worker nodes is assigned exactly one of the plurality of discrete simulation tasks at any given time during operation; analyze results of each of the plurality of discrete simulation tasks as they are completed; and provide the analyzed results as output, is disclosed.
- According to another preferred embodiment, a method for management and tracking of collaborative projects, comprising the steps of: operating, at an automated planning service, a plurality of master nodes, each master node in turn operating a plurality of worker nodes; receiving a plurality of simulation conditions at a master node; constructing a plurality of simulation components based on the received simulation conditions; constructing a planning model based on the received simulation conditions; assigning, using a master node, a plurality of discrete simulation tasks to a plurality of worker nodes, each of the plurality of discrete simulation tasks being based on the constructed simulation components and planning model, wherein each of the plurality of worker nodes is assigned exactly one of the plurality of discrete simulation tasks at any given time during operation; analyzing results of each of the plurality of discrete simulation tasks as they are completed; and providing the analyzed results as output, is disclosed.
- The accompanying drawings illustrate several aspects and, together with the description, serve to explain the principles of the invention according to the aspects. It will be appreciated by one skilled in the art that the particular arrangements illustrated in the drawings are merely exemplary and are not to be considered as limiting of the scope of the invention or the claims herein in any way.
FIG. 1 is a block diagram illustrating an exemplary hardware architecture of a computing device used in various embodiments of the invention.FIG. 2 is a block diagram illustrating an exemplary logical architecture for a client device, according to various embodiments of the invention.FIG. 3 is a block diagram illustrating an exemplary architectural arrangement of clients, servers, and external services, according to various embodiments of the invention.FIG. 4 is a block diagram illustrating an exemplary overview of a computer system as may be used in any of the various locations throughout the systemFIG. 5 is a diagram of an exemplary architecture for a system where streams of input data from one or more of a plurality of sources are analyzed to predict outcome using both batch analysis of acquired data and transformation pipeline manipulation of current streaming data according to an embodiment of the invention.FIG. 6 is a diagram of an exemplary architecture for a linear transformation pipeline system which introduces the concept of the transformation pipeline as a directed graph of transformation nodes and messages according to an embodiment of the invention.FIG. 7 is a diagram of an exemplary architecture for a transformation pipeline system where one of the transformations receives input from more than one source which introduces the concept of the transformation pipeline as a directed graph of transformation nodes and messages according to an embodiment of the invention.FIG. 8 is a diagram of an exemplary architecture for a transformation pipeline system where the output of one data transformation servers as the input of more than one downstream transformation which introduces the concept of the transformation pipeline as a directed graph of transformation nodes and messages according to an embodiment of the invention.FIG. 9 is a diagram of an exemplary architecture for a transformation pipeline system where a set of three data transformations act to form a cyclical pipeline which also introduces the concept of the transformation pipeline as a directed graph of transformation nodes and messages according to an embodiment of the invention.FIG. 10 is a process flow diagram of a method for the receipt, processing and predictive analysis of streaming data using a system of the invention.FIG. 11 is a process flow diagram of a method for representing the operation of the transformation pipeline as a directed graph function using a system of the invention.FIG. 12 is a process flow diagram of a method for a linear data transformation pipeline using a system of the invention.FIG. 13 is a process flow diagram of a method for the disposition of input from two antecedent data transformations into a single data transformation of transformation pipeline using a system of the invention.FIG. 14 is a process flow diagram of a method for the disposition of output of one data transformation that then serves as input to two postliminary data transformations using a system of the invention.FIG. 15 is a process flow diagram of a method for processing a set of three or more data transformations within a data transformation pipeline where output of the last member transformation of the set serves as input of the first member transformation thereby creating a cyclical relationship using a system of the invention.FIG. 16 is a process flow diagram of a method for the receipt and use of streaming data into batch storage and analysis of changes over time, repetition of specific data sequences or the presence of critical data points using a system of the invention.FIG. 17 is a diagram of a computing architecture for a processing system according to one aspect of the present invention.FIG. 18 is a diagram of a computing pipeline architecture for a processing system according to one aspect of the present invention.FIG. 19 is a diagram of a computing operating states for a processing system according to one aspect of the present invention.FIG. 20A-20D is a process flow diagram for a set of processing operations used in a pipeline processing system according to one aspect of the present invention.FIG. 21 is a system diagram detailing the components of a Production Rule System (PRS), according to an embodiment.FIG. 22 is a system diagram illustrating cyclic workflow stages in a pipeline of data analysis, according to an embodiment.FIG. 23 is a block diagram illustrating an exemplary system architecture for automated planning, according to a preferred embodiment.FIG. 24 is a flow diagram illustrating an exemplary overview of a process for automated planning, according to a preferred embodiment.FIG. 25 is a flow diagram illustrating an exemplary process for a single-run AP job.FIG. 26 is a block diagram illustrating an exemplary process for a multiple-run AP job.- The inventor has conceived, and reduced to practice, a distributed automated planning and execution platform for designing, instantiating, and running complex and evolving processes.
- One or more different inventions may be described in the present application. Further, for one or more of the inventions described herein, numerous alternative embodiments may be described; it should be understood that these are presented for illustrative purposes only. The described embodiments are not intended to be limiting in any sense. One or more of the inventions may be widely applicable to numerous embodiments, as is readily apparent from the disclosure. In general, embodiments are described in sufficient detail to enable those skilled in the art to practice one or more of the inventions, and it is to be understood that other embodiments may be utilized and that structural, logical, software, electrical and other changes may be made without departing from the scope of the particular inventions. Accordingly, those skilled in the art will recognize that one or more of the inventions may be practiced with various modifications and alterations. Particular features of one or more of the inventions may be described with reference to one or more particular embodiments or figures that form a part of the present disclosure, and in which are shown, by way of illustration, specific embodiments of one or more of the inventions. It should be understood, however, that such features are not limited to usage in the one or more particular embodiments or figures with reference to which they are described. The present disclosure is neither a literal description of all embodiments of one or more of the inventions nor a listing of features of one or more of the inventions that must be present in all embodiments.
- Headings of sections provided in this patent application and the title of this patent application are for convenience only, and are not to be taken as limiting the disclosure in any way.
- Devices that are in communication with each other need not be in continuous communication with each other, unless expressly specified otherwise. In addition, devices that are in communication with each other may communicate directly or indirectly through one or more intermediaries, logical or physical.
- A description of an embodiment with several components in communication with each other does not imply that all such components are required. To the contrary, a variety of optional components may be described to illustrate a wide variety of possible embodiments of one or more of the inventions and in order to more fully illustrate one or more aspects of the inventions. Similarly, although process steps, method steps, algorithms or the like may be described in a sequential order, such processes, methods and algorithms may generally be configured to work in alternate orders, unless specifically stated to the contrary. In other words, any sequence or order of steps that may be described in this patent application does not, in and of itself, indicate a requirement that the steps be performed in that order. The steps of described processes may be performed in any order practical.
- Further, some steps may be performed simultaneously despite being described or implied as occurring sequentially (e.g., because one step is described after the other step). Moreover, the illustration of a process by its depiction in a drawing does not imply that the illustrated process is exclusive of other variations and modifications thereto, does not imply that the illustrated process or any of its steps are necessary to one or more of the invention(s), and does not imply that the illustrated process is preferred. Also, steps are generally described once per embodiment, but this does not mean they must occur once, or that they may only occur once each time a process, method, or algorithm is carried out or executed. Some steps may be omitted in some embodiments or some occurrences, or some steps may be executed more than once in a given embodiment or occurrence.
- When a single device or article is described, it will be readily apparent that more than one device or article may be used in place of a single device or article. Similarly, where more than one device or article is described, it will be readily apparent that a single device or article may be used in place of the more than one device or article.
- The functionality or the features of a device may be alternatively embodied by one or more other devices that are not explicitly described as having such functionality or features. Thus, other embodiments of one or more of the inventions need not include the device itself.
- Techniques and mechanisms described or referenced herein will sometimes be described in singular form for clarity. However, it should be noted that particular embodiments include multiple iterations of a technique or multiple manifestations of a mechanism unless noted otherwise. Process descriptions or blocks in figures should be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps in the process. Alternate implementations are included within the scope of embodiments of the present invention in which, for example, functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those having ordinary skill in the art.
- As used herein, “graph” is a representation of information and relationships, where each primary unit of information makes up a “node” or “vertex” of the graph and the relationship between two nodes makes up an edge of the graph. Nodes can be further qualified by the connection of one or more descriptors or “properties” to that node. For example, given the node “James R,” name information for a person, qualifying properties might be “183 cm tall”, “DOB Aug. 13, 1965” and “speaks English”. Similar to the use of properties to further describe the information in a node, a relationship between two nodes that forms an edge can be qualified using a “label”. Thus, given a second node “Thomas G,” an edge between “James R” and “Thomas G” that indicates that the two people know each other might be labeled “knows.” When graph theory notation (Graph=(Vertices, Edges)) is applied this situation, the set of nodes are used as one parameter of the ordered pair, V and the set of 2 element edge endpoints are used as the second parameter of the ordered pair, E. When the order of the edge endpoints within the pairs of E is not significant, for example, the edge James R, Thomas G is equivalent to Thomas G, James R, the graph is designated as “undirected.” Under circumstances when a relationship flows from one node to another in one direction, for example James R is “taller” than Thomas G, the order of the endpoints is significant. Graphs with such edges are designated as “directed.” In the distributed computational graph system, transformations within transformation pipeline are represented as directed graph with each transformation comprising a node and the output messages between transformations comprising edges. Distributed computational graph stipulates the potential use of non-linear transformation pipelines which are programmatically linearized. Such linearization can result in exponential growth of resource consumption. The most sensible approach to overcome possibility is to introduce new transformation pipelines just as they are needed, creating only those that are ready to compute. Such method results in transformation graphs which are highly variable in size and node, edge composition as the system processes data streams. Those familiar with the art will realize that transformation graph may assume many shapes and sizes with a vast topography of edge relationships. The examples given were chosen for illustrative purposes only and represent a small number of the simplest of possibilities. These examples should not be taken to define the possible graphs expected as part of operation of the invention
- As used herein, “transformation” is a function performed on zero or more streams of input data which results in a single stream of output which may or may not then be used as input for another transformation. Transformations may comprise any combination of machine, human or machine-human interactions Transformations need not change data that enters them, one example of this type of transformation would be a storage transformation which would receive input and then act as a queue for that data for subsequent transformations. As implied above, a specific transformation may generate output data in the absence of input data. A time stamp serves as an example. In the invention, transformations are placed into pipelines such that the output of one transformation may serve as an input for another. These pipelines can consist of two or more transformations with the number of transformations limited only by the resources of the system. Historically, transformation pipelines have been linear with each transformation in the pipeline receiving input from one antecedent and providing output to one subsequent with no branching or iteration. Other pipeline configurations are possible. The invention is designed to permit several of these configurations including, but not limited to: linear, afferent branch, efferent branch and cyclical.
- A “database” or “data storage subsystem” (these terms may be considered substantially synonymous), as used herein, is a system adapted for the long-term storage, indexing, and retrieval of data, the retrieval typically being via some sort of querying interface or language. “Database” may be used to refer to relational database management systems, but should not be considered to be limited to such systems. Many alternative database or data storage system technologies have been, and indeed are being, introduced, including but not limited to distributed non-relational data storage systems such as Hadoop, column-oriented databases, in-memory databases, and the like. While various embodiments may preferentially employ one or another of the various data storage subsystems available (or available in the future), the invention should not be construed to be so limited, as any data storage architecture may be used according to the embodiments. Similarly, while in some cases one or more particular data storage needs are described as being satisfied by separate components (for example, an expanded private capital markets database and a configuration database), these descriptions refer to functional uses of data storage systems and do not refer to their physical architecture. For instance, any group of data storage systems of databases referred to herein may be included together in a single database management system operating on a single machine, or they may be included in a single database management system operating on a cluster of machines. Similarly, any single database (such as an expanded private capital markets database) may be implemented on a single machine, on a set of machines using clustering technology, on several machines connected by one or more messaging systems, or in a master/slave arrangement. These examples should make clear that no particular architectural approaches to database management is preferred according to the invention, and choice of data storage technology is at the discretion of each implementer, without departing from the scope of the invention as claimed.
- Generally, the techniques disclosed herein may be implemented on hardware or a combination of software and hardware. For example, they may be implemented in an operating system kernel, in a separate user process, in a library package bound into network applications, on a specially constructed machine, on an application-specific integrated circuit (ASIC), or on a network interface card.
- Software/hardware hybrid implementations of at least some of the embodiments disclosed herein may be implemented on a programmable network-resident machine (which should be understood to include intermittently connected network-aware machines) selectively activated or reconfigured by a computer program stored in memory. Such network devices may have multiple network interfaces that may be configured or designed to utilize different types of network communication protocols. A general architecture for some of these machines may be disclosed herein in order to illustrate one or more exemplary means by which a given unit of functionality may be implemented. According to specific embodiments, at least some of the features or functionalities of the various embodiments disclosed herein may be implemented on one or more general-purpose computers associated with one or more networks, such as for example an end-user computer system, a client computer, a network server or other server system possibly networked with others in a data processing center, a mobile computing device (e.g., tablet computing device, mobile phone, smartphone, laptop, and the like), a consumer electronic device, a music player, or any other suitable electronic device, router, switch, or the like, or any combination thereof. In at least some embodiments, at least some of the features or functionalities of the various embodiments disclosed herein may be implemented in one or more virtualized computing environments (e.g., network computing clouds, virtual machines hosted on one or more physical computing machines, or the like).
- Referring now to
FIG. 1 , there is shown a block diagram depicting anexemplary computing device 100 suitable for implementing at least a portion of the features or functionalities disclosed herein.Computing device 100 may be, for example, any one of the computing machines listed in the previous paragraph, or indeed any other electronic device capable of executing software- or hardware-based instructions according to one or more programs stored in memory.Computing device 100 may be adapted to communicate with a plurality of other computing devices, such as clients or servers, over communications networks such as a wide area network a metropolitan area network, a local area network, a wireless network, the Internet, or any other network, using known protocols for such communication, whether wireless or wired. - In one embodiment,
computing device 100 includes one or more central processing units (CPU)102, one ormore interfaces 110, and one or more buses106 (such as a peripheral component interconnect (PCI) bus). When acting under the control of appropriate software or firmware, CPU102 may be responsible for implementing specific functions associated with the functions of a specifically configured computing device or machine. For example, in at least one embodiment, acomputing device 100 may be configured or designed to function as a server system utilizing CPU102,local memory 101 and/orremote memory 120, and interface(s)110. In at least one embodiment, CPU102 may be caused to perform one or more of the different types of functions and/or operations under the control of software modules or components, which for example, may include an operating system and any appropriate applications software, drivers, and the like. - CPU102 may include one or
more processors 103 such as, for example, a processor from one of the Intel, ARM, Qualcomm, and AMD families of microprocessors. In some embodiments,processors 103 may include specially designed hardware such as application-specific integrated circuits (ASICs), electrically erasable programmable read-only memories (EEPROMs), field-programmable gate arrays (FPGAs), and so forth, for controlling operations ofcomputing device 100. In a specific embodiment, a local memory101 (such as non-volatile random access memory (RAM) and/or read-only memory (ROM), including for example one or more levels of cached memory) may also form part of CPU102. However, there are many different ways in which memory may be coupled tosystem 100.Memory 101 may be used for a variety of purposes such as, for example, caching and/or storing data, programming instructions, and the like. - As used herein, the term “processor” is not limited merely to those integrated circuits referred to as a processor, a mobile processor, or a microprocessor, but broadly refers to a microcontroller, a microcomputer, a programmable logic controller, an application-specific integrated circuit, and any other programmable circuit.
- In one embodiment, interfaces110 are provided as network interface cards (NICs). Generally, NICs control the sending and receiving of data packets over a computer network; other types of
interfaces 110 may for example support other peripherals used withcomputing device 100. Among the interfaces that may be provided are Ethernet interfaces, frame relay interfaces, cable interfaces, DSL interfaces, token ring interfaces, graphics interfaces, and the like. In addition, various types of interfaces may be provided such as, for example, universal serial bus (USB), Serial, Ethernet, Firewire, PCI, parallel, radio frequency (RF), Bluetooth, near-field communications (e.g., using near-field magnetics), 802.11 (WiFi), frame relay, TCP/IP, ISDN, fast Ethernet interfaces, Gigabit Ethernet interfaces, asynchronous transfer mode (ATM) interfaces, high-speed serial interface (HSSI) interfaces, Point of Sale (POS) interfaces, fiber data distributed interfaces (FDDIs), and the like. Generally,such interfaces 110 may include ports appropriate for communication with appropriate media. In some cases, they may also include an independent processor and, in some instances, volatile and/or non-volatile memory (e.g., RAM). - Although the system shown in
FIG. 1 illustrates one specific architecture for acomputing device 100 for implementing one or more of the inventions described herein, it is by no means the only device architecture on which at least a portion of the features and techniques described herein may be implemented. For example, architectures having one or any number ofprocessors 103 may be used, andsuch processors 103 may be present in a single device or distributed among any number of devices. In one embodiment, asingle processor 103 handles communications as well as routing computations, while in other embodiments a separate dedicated communications processor may be provided. In various embodiments, different types of features or functionalities may be implemented in a system according to the invention that includes a client device (such as a tablet device or smartphone running client software) and server systems (such as a server system described in more detail below). - Regardless of network device configuration, the system of the present invention may employ one or more memories or memory modules (such as, for example,
remote memory block 120 and local memory101) configured to store data, program instructions for the general-purpose network operations, or other information relating to the functionality of the embodiments described herein (or any combinations of the above). Program instructions may control execution of or comprise an operating system and/or one or more applications, for example.Memory 120 ormemories - Because such information and program instructions may be employed to implement one or more systems or methods described herein, at least some network device embodiments may include nontransitory machine-readable storage media, which, for example, may be configured or designed to store program instructions, state information, and the like for performing various operations described herein. Examples of such nontransitory machine-readable storage media include, but are not limited to, magnetic media such as hard disks, floppy disks, and magnetic tape; optical media such as CD-ROM disks; magneto-optical media such as optical disks, and hardware devices that are specially configured to store and perform program instructions, such as read-only memory devices (ROM), flash memory, solid state drives, memristor memory, random access memory (RAM), and the like. Examples of program instructions include both object code, such as may be produced by a compiler, machine code, such as may be produced by an assembler or a linker, byte code, such as may be generated by for example a Java compiler and may be executed using a Java virtual machine or equivalent, or files containing higher level code that may be executed by the computer using an interpreter (for example, scripts written in Python, Perl, Ruby, Groovy, or any other scripting language).
- In some embodiments, systems according to the present invention may be implemented on a standalone computing system. Referring now to
FIG. 2 , there is shown a block diagram depicting a typical exemplary architecture of one or more embodiments or components thereof on a standalone computing system.Computing device 200 includesprocessors 210 that may run software that carry out one or more functions or applications of embodiments of the invention, such as for example aclient application 230.Processors 210 may carry out computing instructions under control of anoperating system 220 such as, for example, a version of Microsoft's Windows operating system, Apple's Mac OS/X or iOS operating systems, some variety of the Linux operating system, Google's Android operating system, or the like. In many cases, one or more shared services225 may be operable insystem 200, and may be useful for providing common services toclient applications 230. Services225 may for example be Windows services, user-space common services in a Linux environment, or any other type of common service architecture used withoperating system 210. Input devices270 may be of any type suitable for receiving user input, including for example a keyboard, touchscreen, microphone (for example, for voice input), mouse, touchpad, trackball, or any combination thereof.Output devices 260 may be of any type suitable for providing output to one or more users, whether remote or local tosystem 200, and may include for example one or more screens for visual output, speakers, printers, or any combination thereof.Memory 240 may be random-access memory having any structure and architecture, for use byprocessors 210, for example to run software.Storage devices 250 may be any magnetic, optical, mechanical, memristor, or electrical storage device for storage of data in digital form. Examples ofstorage devices 250 include flash memory, magnetic hard drive, CD-ROM, and/or the like. - In some embodiments, systems of the present invention may be implemented on a distributed computing network, such as one having any number of clients and/or servers. Referring now to
FIG. 3 , there is shown a block diagram depicting anexemplary architecture 300 for implementing at least a portion of a system according to an embodiment of the invention on a distributed computing network. According to the embodiment, any number ofclients 330 may be provided. Eachclient 330 may run software for implementing client-side portions of the present invention; clients may comprise asystem 200 such as that illustrated inFIG. 2 . In addition, any number ofservers 320 may be provided for handling requests received from one ormore clients 330.Clients 330 andservers 320 may communicate with one another via one or moreelectronic networks 310, which may be in various embodiments of the Internet, a wide area network, a mobile telephony network, a wireless network (such as WiFi, Wimax, and so forth), or a local area network (or indeed any network topology; the invention does not prefer any one network topology over any other).Networks 310 may be implemented using any known network protocols, including for example wired and/or wireless protocols. - In addition, in some embodiments,
servers 320 may callexternal services 370 when needed to obtain additional information, or to refer to additional data concerning a particular call. Communications withexternal services 370 may take place, for example, via one ormore networks 310. In various embodiments,external services 370 may comprise web-enabled services or functionality related to or installed on the hardware device itself. For example, in an embodiment whereclient applications 230 are implemented on a smartphone or other electronic device,client applications 230 may obtain information stored in aserver system 320 in the cloud or on anexternal service 370 deployed on one or more of a particular enterprise's or user's premises. - In some embodiments of the invention,
clients 330 or servers320 (or both) may make use of one or more specialized services or appliances that may be deployed locally or remotely across one ormore networks 310. For example, one ormore databases 340 may be used or referred to by one or more embodiments of the invention. It should be understood thatdatabases 340 may be arranged in a wide variety of architectures and using a wide variety of data access and manipulation means. For example, in various embodiments one ormore databases 340 may comprise a relational database system using a structured query language (SQL), while others may comprise an alternative data storage technology such as “NoSQL” (for example, Hadoop, MapReduce, BigTable, and so forth). In some embodiments variant database architectures such as column-oriented databases, in-memory databases, clustered databases, distributed databases, key-value stores, or even flat file data repositories may be used according to the invention. It will be appreciated that any combination of database technologies may be used as appropriate, unless a specific database technology or a specific arrangement of components is specified for a particular embodiment herein. Moreover, it should be appreciated that the term “database” as used herein may refer to a physical database machine, a cluster of machines acting as a single database system, or a logical database within an overall database management system. Unless a specific meaning is specified for a given use of the term “database”, it should be construed to mean any of these senses of the word, all of which are understood as a plain meaning of the term “database”. - Similarly, most embodiments of the invention may make use of one or
more security systems 360 and configuration systems350. Security and configuration management are common information technology (IT) and web functions, and some amount of each are generally associated with any IT or web systems. It should be understood that any configuration or security subsystems may be used in conjunction with embodiments of the invention without limitation, unless aspecific security 360 or configuration350 system or approach is specifically required by the description of any specific embodiment. FIG. 4 shows an exemplary overview of acomputer system 400 as may be used in any of the various locations throughout the system. It is exemplary of any computer that may execute code to process data. Various modifications and changes may be made tocomputer system 400 without departing from the broader scope of the system and method disclosed herein.CPU 401 is connected tobus 402, to which bus is also connectedmemory 403,nonvolatile memory 404,display 407, I/O unit 408, and network interface card (NIC)413. I/O unit 408 may, typically, be connected tokeyboard 409, pointingdevice 410,hard disk 412, and real-time clock 411.NIC 413 connects to network414, which may be the Internet or a local network, which local network may or may not have connections to the Internet. Also shown as part ofsystem 400 ispower supply unit 405 connected, in this example, toac supply 406. Not shown are batteries that could be present, and many other devices and modifications that are well known but are not applicable to the specific novel functions of the current system and method disclosed herein. It should be appreciated that some or all components illustrated may be combined, such as in various integrated applications (for example, Qualcomm or Samsung SOC-based devices), or whenever it may be appropriate to combine multiple capabilities or functions into a single hardware device (for instance, in mobile devices such as smartphones, video game consoles, in-vehicle computer systems such as navigation or multimedia systems in automobiles, or other integrated hardware devices).- In various embodiments, functionality for implementing systems or methods of the present invention may be distributed among any number of client and/or server components. For example, various software modules may be implemented for performing various functions in connection with the present invention, and such modules may be variously implemented to run on server and/or client components.
FIG. 23 is a block diagram illustrating an exemplary system architecture for automated planning, according to a preferred embodiment. In various embodiments, an automated planning (AP) system2300 is capable of handling queue-based jobs individually or in batches as multiple-run jobs, with individual worker nodes handling tasks and providing completed work to a master node that evaluates and publishes results, for example using an AKKA™ cluster or similar master/worker cluster operation. This achieves an asynchronous, “eventual agreement” data model wherein worker nodes need not synchronize with each other directly, and completed work is reconciled at the master node; this data model allows asynchronous task completion and eliminates the need for additional time and data throughput to be allocated to synchronization tasks, enabling worker nodes to be dedicated solely to completing an assigned work task. Master and worker nodes may be distributed across a network, and may operate as a federated service that may be accessible to clients from any location, providing a cloud-based AP service.- An automated planning (AP)
service 2320 connects to a plurality ofdatabases 2310a-n, such as a MDTSD for storing time-series data or various query-specific datastores such as (for example, including but not limited to) a SQL or GraphStack storage, or other data storage suitable for storing and providing data to various components of the system as needed. Data may be provided toAP service 2320 via aREST API 2311 to enable initiation, processing, and retrieval of data byAP service 2320.AP service 2320 in turn comprises a plurality of worker nodes2321a-nthat are managed by one of a plurality of master nodes2322a-n, which receives data fromdatabases 2310a-nas well as via REST API2323 from external inputs such as (for example) a user interacting via a command-line interface (CLI)2330. - Master nodes2322a-nprovide work tasks to worker nodes2321a-n, and worker nodes2321a-noperate only to complete a given work task. When not actively processing a given task, a worker node reports to its corresponding master node that it is ready for work, and when a processing task completes the worker node publishes the results to its master node along with a report that it is again ready for new work. In this manner, each individual worker node operates independently to focus on a delegated work task, while master nodes delegate work and collect completed items. This facilitates the “eventual agreement” processing model, as the worker nodes do nothing to synchronize with each other (and may not have any sense of “awareness” of any other worker nodes, interacting exclusively with the master node) while the master node reconciles completed work items as they are received, piecing together individual tasks as a given work job is completed piece-by-piece by the worker nodes. Exemplary processes for performing automated planning using this worker/master architectures are detailed below, with reference to
FIGS. 24-26 . FIG. 5 is a block diagram of an exemplary architecture for asystem 500 for predictive analysis of very large data sets using a distributed computational graph (DCG). According to the embodiment, streaming input feeds510 may be a variety of data sources which may include but are not limited to theinternet 511, arrays of physical sensors512,database servers 513,electronic monitoring equipment 514 and directhuman interaction 515 ranging from a relatively few numbers of participants to a large crowd sourcing campaign. Streaming data from any combinations of listed sources and those not listed may also be expected to occur as part of the operation of the invention as the number of streaming input sources is not limited by the design. All incoming streaming data may be passed through a datafilter software module 520 to remove information that has been damaged in transit, is misconfigured, or is malformed in some way that precludes use. Many of the filter parameters may be expected to be preset prior to operation, however, design of the invention makes provision for the behavior of thefilter software module 520 to be changed as progression of analysis requires through the automation of the system sanity and retrainsoftware module 563 which may serve to optimize system operation and analysis function. The data stream may also be split into two identical sub streams at the datafilter software module 520 with one sub stream being fed into a streaming analysis pathway that includes the transformationpipeline software module 561 of the distributedcomputational graph 560. The other sub stream may be fed to dataformalization software module 530 as part of the batch analysis pathway. The data formalizationmodule 530 formats the data stream entering the batch analysis pathway of the invention into data records to be stored by the inputevent data store 540. The inputevent data store 540 can be a database of any architectural type, but based upon the data model the data store module would be expected to store and retrieve, options using highly distributed storage and map reduce query protocols, of which Hadoop is one, but not the only example, may be generally preferable to relational database schema.- Analysis of data from the input event data store may be performed by the batch event
analysis software module 550. This module may be used to analyze the data in the input event data store for temporal information such as trends, previous occurrences of the progression of a set of events, with outcome, the occurrence of a single specific event with all events recorded before and after whether deemed relevant at the time or not, and presence of a particular event with all documented possible causative and remedial elements, including best guess probability information. It should be recognized that while examples here focus on having stores of information pertaining to time, the use of the invention is not limited to such contexts as there are other fields where having a store of existing data would be critical to predictive analysis of streamingdata 561. The search parameters used by the batch eventanalysis software module 550 are preset by those conducting the analysis at the beginning of the process, however, as the search matures and results are gleaned from the streaming data during transformationpipeline software module 561 operation, providing the system more timely event progress details, the system sanity and retrainsoftware module 563 may automatically update thebatch analysis parameters 550. Alternately, findings outside the system may precipitate the authors of the analysis to tune the batch analysis parameters administratively from outside thesystem data analysis core 560 of the invention should be considered made up of a transformationpipeline software module 561,messaging module 562 and system sanity and retrainsoftware module 563. Themessaging module 562 has connections from both the batch and the streaming data analysis pathways and serves as a conduit for operational as well as result information between those two parts of the invention. The message module also receives messages from those administeringanalyses 580. Messages aggregated by themessaging module 562 may then be sent to system sanity and retrainsoftware module 563 as appropriate. Several of the functions of the system sanity and retrain software module have already been disclosed. Briefly, this is software that may be used to monitor the progress of streaming data analysis optimizing coordination between streaming and batch analysis pathways by modifying or “retraining” the operation of the datafilter software module 520, data formalizationsoftware module 530 and batch eventanalysis software module 540 and thetransformation pipeline module 550 of the streaming pathway when the specifics of the search may change due to results produced during streaming analysis. System sanity and retrainmodule 563 may also monitor for data searches or transformations that are processing slowly or may have hung and for results that are outside established data stability boundaries so that actions can be implemented to resolve the issue. While the system sanity and retrainsoftware module 563 may be designed to act autonomously and employs computer learning algorithms, according to some arrangements status updates may be made by administrators or potentially direct changes to operational parameters by such, according to the embodiment. - Streaming data entering from the outside data feeds510 through the data
filter software module 520 may be analyzed in real time within the transformationpipeline software module 561. Within a transformation pipeline, a set of functions tailored to the analysis being run are applied to the input data stream. According to the embodiment, functions may be applied in a linear, directed path or in more complex configurations. Functions may be modified over time during an analysis by the system sanity and retrainsoftware module 563 and the results of the transformation pipeline, impacted by the results of batch analysis are then output in the format stipulated by the authors of the analysis which may be human readable printout, an alarm, machine readable information destined for another system or any of a plurality of other forms. FIG. 6 is a block diagram of a preferred architecture for a transformation pipeline within a system for predictive analysis of very large data sets using distributedcomputational graph 600. According to the embodiment, streaming input from the datafilter software module first transformation node 620 of the transformation pipeline. Transformation node's function is performed on input data stream and transformedoutput message 625 is sent totransformation node 2630. The progression oftransformation nodes node FIGS. 6-9 ), such uniformity is used for presentation simplicity and clarity and does not reflect necessary operational similarity between transformations within the pipeline. It should be appreciated that one knowledgeable in the field will realize that certain transformations in a pipeline may be entirely self-contained; certain transformations may involve directhuman interaction 630, such as selection via dial or dials, positioning of switch or switches, or parameters set on control display, all of which may change during analysis; other transformations may require external aggregation or correlation services or may rely on remote procedure calls to synchronous or asynchronous analysis engines as might occur in simulations among a plurality of other possibilities. Further according to the embodiment, individual transformation nodes in one pipeline may represent function of another transformation pipeline. It should be appreciated that the node length of transformation pipelines depicted in no way confines the transformation pipelines employed by the invention to an arbitrarymaximum length transform pipeline 660 may be sent back tomessaging software module 562 for predetermined action.FIG. 7 is a block diagram of another preferred architecture for a transformation pipeline within a system for predictive analysis of very large data sets using distributedcomputational graph 700. According to the embodiment, streaming input from a datafilter software module first transformation node 710 of the transformation pipeline. Transformation node's function is performed on input data stream and transformedoutput message 715 is sent totransformation node 2720. In this embodiment,transformation node 2720 has asecond input stream 765. The specific source of this input is inconsequential to the operation of the invention and could be another transformation pipeline software module, a data store, human interaction, physical sensors, monitoring equipment for other electronic systems or a stream from the internet as from a crowdsourcing campaign, just to name afew possibilities 760. In an alternative embodiment, asecond input stream 760 may contain a specification of data context that is preserved from the first stream into anode 2720, the shared data context between the inputs of atransformation node 720 allowing the services or streams that send data to a node to share common meaning and enable faster or different methods of processing, including finding correlations or causative tendencies between data from two sources or streams, in the case of a shared data context. It is not required that a secondary, tertiary, or further source ofdata 760 be functioning as input to specifically the second node in thegraph 720, and there may be a plurality of other datastreams feeding into one or several of different nodes in the graph. Functional integration of a second input stream into one transformation node requires the two input stream events be serialized. The invention performs this serialization using a decomposable transformation software module (not shown), the function of which is described below, referring toFIG. 13 . While transformation nodes are described according to various embodiments as uniform shape (referring toFIGS. 6-9 ), such uniformity is used for presentation simplicity and clarity and does not reflect necessary operational similarity between transformations within the pipeline. It should be appreciated that one knowledgeable in the field will realize that certain transformations in a pipeline may be entirely self-contained; certain transformations may involve directhuman interaction 630, such as selection via dial or dials, positioning of switch or switches, or parameters set on control display, all of which may change during analysis; other transformations may require external aggregation or correlation services or may rely on remote procedure calls to synchronous or asynchronous analysis engines as might occur in simulations among a plurality of other possibilities. Further according to the embodiment, individual transformation nodes in one pipeline may represent function of another transformation pipeline. It should be appreciated that the node length of transformation pipelines depicted in no way confines the transformation pipelines employed by the invention to an arbitrarymaximum length outputs Output 755 of the last transformation node and by extension, the transform pipeline,750 may be sent back tomessaging software module 562 for pre-decided action.FIG. 8 is a block diagram of another preferred architecture for a transformation pipeline within a system for predictive analysis of very large data sets using distributedcomputational graph 700. According to the embodiment, streaming input from a datafilter software module first transformation node 810 of the transformation pipeline. Transformation node's function is performed on input data stream and transformedoutput message 815 is sent totransformation node 2820. In this embodiment,transformation node 2820 sends itsoutput stream transformation pipelines transformation node 820 requires that the two output stream events be serialized. The invention performs this serialization using a decomposable transformation software module (not shown), the function of which is described below, referring toFIG. 14 . While transformation nodes are described according to various embodiments as uniform shape (referring toFIGS. 6-9 ), such uniformity is used for presentation simplicity and clarity and does not reflect necessary operational similarity between transformations within the pipeline. It should be appreciated that one knowledgeable in the field will realize that certain transformations in pipelines, which may be entirely self-contained; certain transformations may involve directhuman interaction 630, such as selection via dial or dials, positioning of switch or switches, or parameters set on control display, all of which may change during analysis; other transformations may require external aggregation or correlation services or may rely on remote procedure calls to synchronous or asynchronous analysis engines as might occur in simulations, among a plurality of other possibilities.- Further according to the embodiment, individual transformation nodes in one pipeline may represent function of another transformation pipeline. It should be appreciated that the node number of transformation pipelines and their
outputs maximum length transform pipeline 850 may be sent back tomessaging software module 562 for contemporary enabled action. FIG. 9 is a block diagram of another preferred architecture for a transformation pipeline within a system for predictive analysis of very large data sets using distributedcomputational graph 700. According to the embodiment, streaming input from a datafilter software module first transformation node 910 of the transformation pipeline. Transformation node's function may be performed on an input data stream and transformedoutput message 915 may then be sent totransformation node 2920. Likewise, once the data stream is acted upon bytransformation node 2920, its output is sent totransformation node 3930 using itsoutput message 925 In this embodiment,transformation node 3930 sends its output stream back to transformnode 1935,910 forming a cyclical relationship betweentransformation nodes 1910,transformation node 2920 andtransformation node 3930. Upon the achievement of some gateway result, the output of cyclical pipeline activity may be sent to downstream transformation nodes within thepipeline FIG. 15 . While transformation nodes are described according to various embodiments as uniform shape (referring toFIGS. 6-9 ), such uniformity is used for presentation simplicity and clarity and does not reflect necessary operational similarity between transformations within the pipeline. It should be appreciated that one knowledgeable in the field will appreciate that certain transformations in pipelines, may be entirely self-contained; certain transformations may involve directhuman interaction 630, such as selection via dial or dials, positioning of switch or switches, or parameters set on control display, all of which may change during analysis; still other transformations may require external aggregation or correlation services or may rely on remote procedure calls to synchronous or asynchronous analysis engines as might occur in simulations, among a plurality of other possibilities. Further according to the embodiment, individual transformation nodes in one pipeline may represent the cumulative function of another transformation pipeline. It should be appreciated that the node number of transformation pipelines depicted in no way confines the transformation pipelines employed by the invention to an arbitrarymaximum length last transformation node 960 and by extension, thetransform pipeline 955 may be sent back tomessaging software module 562 for concomitant enabled action.FIG. 17 is a diagram of a computing architecture for a processing system according to one aspect of the present invention. An environmental orchestration anddata processing engine 1700 permits domain experts to directly capture their knowledge via a user interface with domain agnostic building blocks. These modular components can be built and extended by programmers to satisfy a number of use cases without a need to understand how they will be used in a specific implementation. AnEnvironmental Orchestration component 1711 andData Processing component 1712, coupled together1701, allow for both flexibility and tight coupling between all the actions needed to set up resources and perform analytical tasks.- The processing tasks are divided between data processing, orchestration, and system tasks. The data processing tasks provide a plug and play style data processing backend and orchestrates work against that backend. In a preferred embodiment, a
data management backend 1702 provides the backend processing functionality that consumes data streams for processing. A variety of data management and stream processing backends may be utilized, including APACHE FLINK™, SPARK™, and APACHE BEAM™. - Data streams may use JavaScript Object Notation (JSON) as a lightweight data-interchange format that is easy for humans to read and write, as well as for machines to parse and generate. It is based on a subset of the JavaScript Programming Language, Standard ECMA-262 3rd Edition-December 1999. JSON is a text format that is completely language independent but uses conventions that are familiar to programmers of the C-family of languages, including C, C++, C#, Java, JavaScript, Perl, Python, and many others.
- For example, a
data management backend 1702 is a framework and distributed processing engine for stateful computations over unbounded and bounded data streams. adata management backend 1702 has been designed to run in all common cluster environments, perform computations at in-memory speed and at any scale. a data management backend's architecture may use both Process Unbounded and Bounded Data. Any kind of data is produced as a stream of events. Credit card transactions, sensor measurements, machine logs, or user interactions on a website or mobile application, all of these data are generated as a stream. Data can be processed as unbounded or bounded streams. - Unbounded streams have a start but no defined end. They do not terminate and provide data as it is generated. Unbounded streams must be continuously processed, i.e., events must be promptly handled after they have been ingested. It is not possible to wait for all input data to arrive because the input is unbounded and will not be complete at any point in time. Processing unbounded data often requires that events are ingested in a specific order, such as the order in which events occurred, to be able to reason about result completeness.
- Bounded streams have a defined start and end. Bounded streams can be processed by ingesting all data before performing any computations. Ordered ingestion is not required to process bounded streams because a bounded data set can always be sorted. Processing of bounded streams is also known as batch processing. Pipelines and stages herein may utilize both types of data.
- Orchestration tasks directly handle serializing the Pipeline and Stages, monitoring of active Pipelines and submission of new Pipelines, as well as making requests to 3rd parties for resources to be allocated as needed. These resources may be provided within a single system, a collections of interconnected processing systems operating together within a data centers, and cloud based resources provided by parties over the internet such as Amazon Web Services and Microsoft Azure. All similar could computing services may be used to provide all or part of a pipeline's stages as needed with data being transferred by addressing the particular resources by its IP address.
- System tasks include monitoring, metadata and recovery tasks to provide hooks between a pipeline and the
controlling system 1703 itself to enable it to monitor, pull metadata about multiple pipelines running in sequence and facilitate recovery when pipelines fail, or services that fail. These tasks are needed because thecontrolling system 1703 does not possess a direct feed into the data as it is being processed. - While APACHE FLINK™ is one of many streaming data processing engines, it should be recognized that APIs used to construct the states typically provide functionality that is extensible enough to utilize other processing engines of streaming data such as SPARK™, APACHE BEAM™, and similar stream data processing engines. Additionally, data sinks and data sources may occur any place in the directed graph. Each data sink and data source maybe specified by a declarative formalism embodiment within a workflow such that an entire orchestration workflow may be expressed within the overall workflow.
- This architecture permits the various stages in a workflow to be modularly constructed in which each stage is separately implemented using a declarative definition of a streaming analytics processing workflow. As long as a stage accepts and consumes and then generates and produces a data stream in a common format, any implementation of a particular stage may be used.
FIG. 18 is a diagram of a computing pipeline architecture for a processing system according to one aspect of the present invention. The unit by which this is measured is aPipeline 1800. Apipeline 1800 represents a use case and is the high level application. Different pipelines, as well as components within a pipeline, can work in tandem, allowing for even larger logical applications to be made. Pipelines are constructed of more primitive types called stages. For example, a pipeline shown inFIG. 18 illustrates a sequence of stages running in parallel. All incoming state is obtained by a source stage,stage 11801. This data is provided to three separate sequences of stages, stage3-41811-1812,stage 21802, and stages5-71821-1823. Each of these stages may be processing and sink stages as three sets of results are generated.- A
pipeline 1800 is defined as a computing structure for housing for all the Stages used to construct the pipeline, where the pipeline of stages is represented as a DG (Directed Graph). This has three basic states, running, suspended, deleted. The difference between suspended and deleted is that the suspended state stops processing but doesn't trigger the post conditions, while deleted stops the processing and triggers the post conditions. Pipelines are comprised of four types of Stages. Pipeline 1800 may also be constructed using cyclic workflows of stages3-41811-1812 and stages15-161831-1832. These cyclic workflows may be created using the same messaging fabric in a source/sink used to define all other workflows. This arrangement makes the expressive capability of this streaming analytics engine a full directed graph rather than merely directed acyclic graphs of competing formalisms. One possible example of a cycle would be to have a source stage that consumes from a Kafka topic while a separate sink stages passes messages to the same Kafka topic, thus creating a cycle.- An alternative arrangement and use of the workflow stages is to functionally decompose the workflow stages, and allow them to be embedded in other workflows as single stages, for instance having a workflow with steps A, B, C, and D, embedding another 3-step workflow with steps E, F, and G, inbetween steps B and C, such that the first workflow of processing data is now comprised of steps A, B, (E, F, G), C, and D. This modularity and functional decomposition of data workflows comprises a possible alternative arrangement of the disclosed system, but is not limiting or the only alternative arrangement that may be possible.
- Environmental Conditions correspond parameter and processing conditions a stage is going to need exist to be able to run in processing components. This also includes the reverse process. These are known as the Setup and Teardown Phase. These Conditions are defined by the Stage itself. Environment Stages are a specialized type of stage that contains only these post-conditions and pre-conditions.
- Stages a simple processing task before the processing of a particular set of data is passed to another stage to perform a next step in the process. This architecture provides separate units of work that may be arranged conceptually for users of this system. This architecture also provides a mechanism for a level of abstraction for the operations performed by every stage, such as health metrics and alerting. Stages come in three basic flavors: source stage, transformation stage, and sink stages. A source stage controls how a pipeline getting its data, including its source location, format, and similar conditions. A transformation stage performs operations to manipulating the data received by the pipeline from a source stage. A sink stages controls where any resulting data is stored following its processing through a pipeline, which also includes its location, format, and similar conditions. Additionally, environment stages may also be part of a pipeline. These stages define and manipulate operating conditions defined above as environmental conditions.
- A stage, such as
stage 61822, withinpipeline 1800 may itself be constructed using a workflow defined in exactly the same way. Data entersstage 151831 as a data stream and exits as a data stream in which the number of processing steps implemented as a separately defined workflow pipeline used as modular stage element. Downstream stages, such asstage 71823, does not know whether the data it receives is from a self-contained implementation ofstage 61822, or from an embedded workflow such as fromstage 171833. Hierarchical arrangements of workflows in such a manner permits construction of complex workflow from a combination of less complex workflows. All of this configuration of workflows may be defined in the declarative form described herein, and may use stages implemented in different backend processing engines such as Flink, Spark, Beam or similar data streaming processing technologies. - In order to support such modular functionality,
workflow pipeline 1800 utilizes a common data context permitting easy data exchange and integration of stages implemented in the various processing engines without complication. As noted above, use of a common data exchange format, such as JSON, will assist this modularity. Also, data may be specified using a common set of terms to permit ease of interoperability. A simple example would be to transform all incoming data streams into a standard set of values. For example, data such as distance, temperature, and time (zone) may be provided in various units. By transforming the data into a common set of units, all workflows may interoperate without issue. Data may be retransformed into a set of units useful by a user once the processing is otherwise completed. - It is possible to use the disclosed system, for instance, for the purposes of Complex Event Processing (CEP), which entails real-time processing of event datastreams, through the use of workflow pipelines to extract and analyze important data from a datastream to determine characteristics about an event.
FIG. 19 is a diagram of acomputing operating states 1900 for a pipeline according to one aspect of the present invention. Apipeline 1800, and its component stages, will operate in one of a set of possible operating states. The pipeline begins in anidle state 1901 once it has been loaded into computing resources. No data processing operations occur in this state. The pipeline next enters a startedstate 1902 when the pipeline is launched. From here, the pipeline can transition to a runningstate 1903 to go to a stoppedstate 1906. Data is processed through each of the stages in the pipeline while in the runningstate 1903.- When a set of data has been completely processed, the pipeline can go to a paused
state 1904 or a stopped state. In both cases, data processing is halted. From a pausedstate 1904, the data processing may resume from its last point in the data by restarting the pipeline to return it to a runningstate 1903. - From a stopped
state 1906, the pipeline may enter a deletedstate 1907 when its stages and computing components are removed from the computing resources. The pipeline enters an updatedstate 1905 either when changes are made to the existing graph defining the data flow within the pipeline or when a base docker image used to create the pipeline changes that requires changing to existing pipelines. The stopped pipeline is reconfigured in the update state to permit the new definition for the pipeline to operate on data when the pipeline returns to a runningstate 1903 from theupdate state 1905. FIG. 21 is a system diagram detailing the components of a Production Rule System (PRS), according to an embodiment. Aclient 2105 computer connects to a production rule system (PRS) 2110, via aREST API 2111 over a network. A PRS is a rule system which enables many different functionalities, including making external function calls to domain-specific oracles, providing for generalization of semantic and datastream processing rules and preventing rule creep when defining multiple transitivity properties, allowing for scalar value comparisons of data (comparing ages, distances, etc.), allowing for aggregation of facts and rules from different knowledge bases, graphs, or both, allowing for JSON conversion of rules to and from a GraphStack with a universally unique identifier (UUID), providing the ability to instantiate nodes with specified properties in a GraphStack, provide for a message queue through a Command-Line Interface (CLI) or Graphical User Interface (GUI), and rule building through an API, and allowing for new rules and modified rules to be updated with a real-time visualization. AREST API 2111 provides the forward-facing access toPRS 2110 functionality for aclient 2105, the PRS further comprising a set ofcore components 2120 which operate a further set of construction andevaluation protocols 2130. The construction and evaluation protocols include adata parser 2131,data evaluator 2132, anddata constructor 2133. The remainingcore components 2120 include at least anengine 2121 which drives the overall system and receives semantic data from adata construction component 2133, forwarding processed data to afact registry interface 2122 and anPRS client 2112. Afact registry interface 2122 may register new data selectively or automatically with aknowledge base 2140 which includes a directed knowledge graph and a multidimensional time series database (MDTSDB), and communicate registered data and the result of attempts to register new data with thePRS engine 2121. AnPRS engine 2121 operates the construction andevaluation protocols 2130 to parse data sent through theREST API 2111, and sends results and further queries forbackend oracles 2150 to aPRS client 2112. AnPRS client 2112 represents thePRS system 2110 communicating withbackend oracles 2150 which in turn send the results of these modified queries to theclient 2105, thus completing the cycle and allowing therule system 2110 to act as a modular, integrable front-end to other systems for semantic data and API call processing. As an example of a type of rule that might be created by the PRS, the PRS may declaratively specify windowed rules, wherein rules may be established for events occurring within a given time window. For example, a windowed rule may be established that counts the number of login attempts made within a two-minute time window. The window may be a “tumbled” or “sliding” window that repeatedly refreshes on a periodic basis to apply the rule to the time window just prior to the refresh.- It is possible to use the disclosed system, for instance, for the purposes of Complex Event Processing (CEP), which entails real-time processing of event datastreams, through the use of workflow pipelines to extract and analyze important data from a datastream to determine characteristics about an event.
- Exactly-once semantics settings may be preserved according to some embodiments when registering a new fact or
datapoint 2122 in aknowledge base 2140, such that appearance of one semantically similar or identical datapoint in future processed data may achieve idempotency and cause an effect in the system only the first time it is encountered, but not subsequent times, such as when certain forms of machines have an “ON” and “OFF” switch respectively, wherein the “ON” switch does not perform any other actions after being pressed an initial time, until the device is turned “OFF.” For instance, an event datastream may be processed with semantic learning and examination that contains reference to a temperature of 72 degrees in a specific geographical area. If that same information is processed again, with exactly-once semantics enabled for this datapoint, then subsequent occurrences of the same area having 72 degrees of temperature will not cause a change in the system or a new event to be catalogued, until the temperature in that area changes to something other than 72, such as71, at which point the temperature shifting back to72 will constitute a logged event. In other embodiments, the idempotency may mean that even after a change from the exactly-once occurrence, the occurrence will not trigger a new event. - The
oracles 2150 may comprise any plurality or combination of services and technologies and components, which are utilized for database storage and data stream processing, which thePRS 2110 may communicate with to help with backend processing. According to an embodiment, a database may be included either in theoracles 2150 backend or in theknowledge base 2140, or both, to support the integration of fixed-point rule semantics, providing for analysis of data and semantic data especially by comparison to a fixed point after refinement using machine learning. FIG. 22 is a system diagram illustrating cyclic workflow stages in a pipeline of data analysis, according to an embodiment. Aclient 2210 system sends a query or batch of data for processing to one of four possible workflow stages, either anenvironmental stage 2220, asource stage 2230, atransformation stage 2240, or asink stage 2250. All workflow stages may feed into other workflow stages as shown by directional arrows, or at any point may forward the data from processing in the specified workflow stage back to the client for viewing, without forwarding to another workflow stage. Notably, anenvironmental data stage 2220 is the only workflow stage capable of transmitting data as-needed between all three of the other workflow stages. An environmental workflow stage is utilized when environmental variables, settings, and initializations must be set, for instance initialization of other workflow stages, or of knowledge graph nodes, or other environmental attributes of interest. A source stage ofworkflow 2220 is where data is analyzed to determine, broadly speaking, the origin and acquisition of the data, before either returning the result of the workflow immediately to theclient 2210 or continuing to atransformation stage 2240. A transformation stage ofworkflow 2240 is where data may be manipulated, and represents such workflow steps and functionality as starting a data pipeline, shutting down a data pipeline, and editing a data pipeline for the flow and processing of data as required. This stage of the workflow may return to theclient 2210 or continue on to adata sink stage 2250, which includes functions regarding where to put or send data after processing, or where to send data as received directly from aclient 2210. The workflow diagram as shown illustrates a cyclical nature wherein data and operations can be accomplished in one of several workflow stages, forwarded either to another workflow stage or returned back to the client, and repeated, until a client no longer desires to operate according to the defined workflow.- A novel, declarative domain-specific language (DSL) may be utilized in the workflow cycle. According to a preferred embodiment, several functions of a novel DSL may be utilized, including a capability for bidirectional dependencies on operations (for instance, “A->B” may be used to specify B depending on A before executing, or “B<-A” for the same), channel or domain-specific directional dependencies (for instance, “A->(“EXAMPLE”,B)” may be interpreted as B has a dependency on A's EXAMPLE signal, channel, or argument), multi-argument support (for instance, “A->(set(“EXAMPLE”,“EXAMPLE 2”),B)), and may be modular, for new language definitions and uses to be defined as needed.
FIG. 24 is a flow diagram illustrating an exemplary overview of a process for automated planning, according to a preferred embodiment. According to the embodiment, a distributed computational graph (DCG)2410 receives system observations from a system observation engine2420. These system state observations are used by the DCG to determine a set of execution instructions for a planning task, which are provided to anAP service 2320 for execution.AP service 2320 retrieves aninitial state 2430 and set ofobjectives 2440 fromstorage 2310a-n, to form the boundary conditions for the work task (that is, what the system state looks like at the beginning of execution, and what it needs to look like when execution has completed). Additional input may also be received or collected fromexternal sources 2450, such as user input or online content retrieved through RESTful APIs. The execution instructions are then broken up into discrete work tasks by master nodes within the AP service2320 (as described above, with reference toFIG. 23 ) and work tasks are then assigned to worker nodes operating within the AP service (as described above inFIG. 23 ). When all work tasks have concluded, the AP service produces a set of execution plans and policies that are then provided to theDCG 2410, which in turn produces a set of actions to be taken based on the plans and policies. In this manner, a concrete action plan is produced from state observations and machine learning through the use of DCG pipelines and parallel work processing using the AP service worker nodes, automating the planning process and producing a clear path to reach a desired end state.FIG. 25 is a flow diagram illustrating an exemplary process for a single-run AP job. According to a single-run AP job process, when tasked with a set of execution instructions anAP service 2320 first validates allinput data 2501. This includes (but is not limited to) validating known initial information about the world and system (these describe environmental conditions and context in which the job is being run), objectives for the work job, available resources, constraints on job execution, and initial state expectation (that is, what the system anticipates the starting state to look like).AP service 2320 then constructs a plurality of component models and aplanning instance 2502, which serve as a simulation model for developing work tasks within the context of the specific job. Work task execution then comprises amulti-step operation 2510 utilizing worker nodes to process discrete tasks in parallel, beginning with seeding the newly-constructed planning instance with parameterized models2511; in other words, the instance is populated with simulated models of various factors such as (for example, including but not limited to) environmental factors or job constraints as described above, based on the component models generated by the master nodes (which are provided to worker nodes as needed by their respective master nodes). Execution is then initialized2512, and master nodes assign individual work tasks to worker nodes for processing. Results of work task execution are collected and analyzed bymaster nodes 2513, and any uncertainty estimation is performed2514 as appropriate. The final results, including any uncertainty values to place the results in the proper context for decision making, are then provided to therequestor 2515.FIG. 26 is a block diagram illustrating an exemplary process for a multiple-run AP job. In a multiple-run job, execution does not conclude immediately when a single AP processing job is complete; instead, additional runs are executed iteratively until a plurality of end conditions (such as a timer, a specified number of runs, or a specific desired outcome is generated) are met. In a multiple-run job, after initial validation2601 a plurality of end conditions are specified2602. Initial parameters and planning instance are then constructed2603, and execution of the simulation by worker nodes then proceeds2510. As results are collected2604, analysis now includes determining if end conditions have been met2604. When the required end conditions are met, such as a specified number of runs, master nodes are instructed to stop assigning new work tasks to worker nodes; this effectively halts the execution once all pending tasks have completed, without needing to send multiple stop instructions or synchronize activity between nodes. If a worker node is not assigned work, it simply waits for the next task to be assigned; worker nodes need not have any awareness of the state of an overall AP job, and simply carry out individual tasks as they are assigned. When execution has concluded, results are published to therequestor 2605.FIG. 10 is a process flow diagram of amethod 1000 for predictive analysis of very large data sets using the distributed computational graph. One or more streams of data from a plurality of sources, which includes, but is in no way not limited to, a number of physical sensors, web based questionnaires and surveys, monitoring of electronic infrastructure, crowd sourcing campaigns, and direct human interaction, may be received bysystem 1001. The received stream is filtered1002 to exclude data that has been corrupted, data that is incomplete or misconfigured and therefore unusable, data that may be intact but nonsensical within the context of the analyses being run, as well as a plurality of predetermined analysis related and unrelated criteria set by the authors. Filtered data may be split into two identical streams at this point (second stream not depicted for simplicity), wherein one sub stream may be sent forbatch processing 1600 while another sub stream may be formalized1003 fortransformation pipeline analysis source 1300 or receive no input at all as would a transformation acting as a timestamp. According to the embodiment, individual transformations, may not modify the data as would be encountered with a data store acting as a queue fordownstream transformations - According to the embodiment, individual transformations may provide output to more than one
downstream transformation 1400. This ability lends itself to simulations where multiple possible choices might be made at a single step of a procedure all of which need to be analyzed. While only a single, simple use case has been offered for each example, in each case, that example was chosen for simplicity of description from a plurality of possibilities, the examples given should not be considered to limit the invention to only simplistic applications. Last, according to the invention, transformations in a transformation pipeline backbone may form a linear, a quasi-linear arrangement or may be cyclical1500, where the output of one of the internal transformations serves as the input of one of its antecedents allowing recursive analysis to be run. The result of transformation pipeline analysis may then be modified by results frombatch analysis 1005 of thedata stream 1600 andoutput 1006 in format predesigned by the authors of the analysis with could be human readable summary printout, human readable instruction printout, human-readable raw printout, data store, or machine encoded information of any format that may be used in further automated analysis or action schema. FIG. 11 is a process flow diagram of amethod 1100 for an embodiment of modeling thetransformation pipeline module 561 of the invention as a directed graph using graph theory. According to the embodiment, theindividual transformations transformation pipeline E 1107.FIG. 12 is a process flow diagram of amethod 1200 for one embodiment of alinear transformation pipeline 1201. This is the simplest of configurations as the input stream is acted upon by thefirst transformation node 1202 and the remainder of the transformations within the pipeline are then performed sequentially1202,1203,1204,1205 for the entire pipeline with no introduction of new data internal to the initial node or splitting output stream prior to last node of thepipeline 1205. The result of the transformation pipeline is then sent back out to any message and output processes1206. This configuration is the current state of the art for transformation pipelines and is the most general form of these constructs. Linear transformation pipelines require no special manipulation to simplify the data pathway and are thus referred to as non-decomposable. The example depicted in this diagram was chosen to convey the configuration of a linear transformation pipeline and is the simplest form of the configuration felt to show the point. It in no way implies limitation of the invention.FIG. 13 is a process flow diagram of amethod 1300 for one embodiment of a transformation pipeline where onetransformation node 1307 in a transformation pipeline receives data streams from twosource transformation nodes 1301. The invention handles this transformation pipeline configuration by decomposing or serializing the input events1302-1303,1304-1305 heavily relying on post transformation function continuation. The results ofindividual transformation nodes destination transformation node 1306 and placed into a single specialized datastorage transformation node 1303,1305 (shown twice as process occurs twice). The combined results then retrieved from thedata store 1306 and serve as the input stream for the transformation node within thetransformation pipeline backbone source nodes FIG. 14 is a process flow diagram of amethod 1400 for one embodiment of a transformation pipeline where onetransformation node 1402 sends output to asecond node 1403 in a transformation pipeline, which then may send output data stream to twodestination transformation nodes output events 1404,1405-1406,1407-1408. The results of thesource transformation node 1403 just antecedent to thedestination transformation nodes 1406 and placed into a single specialized datastorage transformation node data store 1404 and serves as the input stream for the transformation nodes twodownstream transformation pipeline destination nodes FIG. 15 is a process flow diagram of amethod 1500 for one embodiment of a transformation pipeline where the topology of all or part of the pipeline is cyclical1501. In this configuration the output stream of onetransformation node 1504 acts as an input of an antecedent transformation node within thepipeline 1502 serialization or decomposition linearizes this cyclical configuration by completing the transformation of all of the nodes that make up asingle cycle data store 1505. That result of a cycle is then reintroduced to the transformation pipeline as input to the first transformation node of thecycle 1506. As this configuration is by nature recursive, special programming to unfold the recursions was developed for the invention to accommodate it. The example depicted in this diagram was chosen to convey the configuration of transformation pipelines with individual transformation nodes that for acyclical configuration FIG. 16 is a process flow diagram of amethod 1600 for one embodiment of the batch data stream analysis pathway which forms part of the invention and allows streaming data to be interpreted with historic context. One or more streams of data from a plurality of sources, which includes, but is in no way not limited to, a number of physical sensors, web based questionnaires and surveys, monitoring of electronic infrastructure, crowd sourcing campaigns, and direct human interaction, is received by thesystem 1601. The received stream may be filtered1602 to exclude data that has been corrupted, data that is incomplete or misconfigured and therefore unusable, data that may be intact but nonsensical within the context of the analyses being run, as well as a plurality of predetermined analysis related and unrelated criteria set by the authors.Data formalization 1603 for batch analysis acts to reformat the stream data for optimal, reliable use during analysis. Reformatting might entail, but is not limited to: setting data field order, standardizing measurement units if choices are given, splitting complex information into multiple simpler fields, and stripping unwanted characters, again, just to name a few simple examples. The filtered and formalized stream is then added to a distributeddata store 1604 due to the vast amount of information accrued over time. The invention has no dependency for specific data stores or data retrieval model. During transformation pipeline analysis of the streaming pipeline, data stored in the batch pathway store can be used to track changes in specifics of the data important to the ongoing analysis over time, repetitive data sets significant to the analysis or the occurrence of critical points ofdata 1605. The functions ofindividual transformation nodes 620 may be saved and can be edited also all nodes of atransformation pipeline 600 keep a summary or summarized view (analogous to a network routing table) of applicable parts of the overall route of the pipeline along with detailed information pertaining to adjacent two nodes. This framework information enables steps to be taken and notifications to be passed ifindividual transformation nodes 640 within atransformation pipeline 600 become unresponsive during analysis operations. Combinations of results from the batch pathway, partial and streaming output results from the transformation pipeline, administrative directives from the authors of the analysis as well as operational status messages from components of the distributed computational graph are used to perform system sanity checks and retraining of one or more of the modules of thesystem 1606. These corrections are designed to occur without administrative intervention under all but the most extreme of circumstances with deep learning capabilities present as part of the system manager and retrainmodule 563 responsible for this task.FIG. 20A-20D is a process flow diagram for a set of processing operations used in a pipeline processing system according to one aspect of the present invention. The controllingsystem 1703 communicates with and controls the operation of a pipeline using a set of API commands that include Post, Get, Delete, and Put commands.FIG. 20A illustrates the operation of thePost 2001 commands. The Post commands include an api/pipelines post and an api/pipelines/validate commands.- The POST/api/pipelines is a command having a content type: ‘application/json.’ This command is the entry point. It creates a new pipeline in the database of pipelines but does not start the pipeline. To start the pipeline, call ‘GET . . . /env’ and ‘GET . . . /data’ commands described below. Invalid pipelines may be saved at this point, future calls to this pipeline will be validated as part of the operation of the command.
- The command has the following payload fields: ‘pipeline;’ (required): ‘stageGraphBuilder’-a JSON representation of a valid pipeline; (required): ‘version’—the system version expected. An error will occur if the manager's version is different; (optional): ‘uuid’—If none is provided one will be created and returned in the response payload; (optional): ‘name’—A human readable name for the pipeline, uniqueness is not enforced; (optional): ‘description’—A description for end users; and (optional): ‘tags’—Keywords or terms associated with the pipeline (these tags are stored in an array). In operation the command receives the
command 2011 and gets data from thedata store 2012 before deciding if the pipeline in question exists2013 in the database. If it does determine the pipeline exists, this pipeline is rejected2014 as already existing. If not, the data is data store is updated2015 and if successful2016, and a201 response with and id==UUID is returned2017. - The POST/api/pipelines/validate is a command having a content type: ‘application/json.’ This command validates a pipeline. A pipeline with no environmental stages and no data processing stages is considered invalid. The command uses payload fields: ‘pipeline’ (required): See [‘POST /api/pipelines’](#post-apipipelines). An example response is:
- Example Response (200 OK):
{ “pipelineId”:“038bf27f-52f0-40cf-95db-b70b83ade772”, “invalidStages”:[ ], “statusCode”:200 } - The command is received2021 and the pipeline is deserialized2022 and a pipeline validation call is made2023. A
response 200 is returned with a list if invalid stages and paths are found2024. FIGS. 20B and 20C illustrate the operation of a set ofGet 2002 and Post commands. APACHE FLINK™ will be used throughout the figures to refer to a data management backend. The GET/api/pipelines?tag=A&tag=B’ is a command having a content type: ‘application/json.’ This command gets the pipelines that are associated with the provided tag(s). An example response (200 OK):{ “pipelineId”:null, “data”:[ { “name”:“pipeline1”, “description”:null, ... }, { “name”:“pipeline2”, “description”:null, ... } ] “statusCode”:200 } - The GET/api/pipelines/{uuid} command is a command having a content type: ‘application/json.’ The command gets the pipeline previously posted pipeline from the database. The command is received2031 and data is obtained from the
data store 2032. If the pipeline exists2033 in the data base, a pipeline definition is returned2035; otherwise areject 404 pipeline not found is returned2034. - The POST/api/pipelines/{uuid}/env/start is a command that calls the environmental setup for a pipeline. An example response (202 Accepted):
{ “pipelineId”:“2db14f86-29c4-4067-a7ac-e05c24035c3a”, “data”:“Environmental setup for pipeline [2db14f86-29c4-4067-a7ac-e05c24035c3a] started”, “statusCode”:202 } - The command is received2041 and data is obtained from the
data store 2042. If the pipeline exists2043 in the data base, a request accepted is returned2044; otherwise areject 404 pipeline not found is returned2034. - The POST/api/pipelines/{uuid}/env/status command returns the statuses of the environmental stages. An example response (200 OK):
{ “pipelineId”: “51afaae4-ddce-42af-ba0a-f341075e412b”, “data”: [{ “uuid”: “bc63f730-ed89-4124-bae9-c31c378802cc”, “state”: “SUCCESS” }, { “uuid”: “a36dda4e-l7fe-4afD-a745-0ea4dc3e948c”, “state”: “SUCCESS” }], “statusCode”: 200 } - The command is received2051 and data is obtained from the
data store 2052. If the pipeline exists2053 in the data base, a stage ID and status is obtained2054 and returned2055; otherwise areject 404 pipeline not found is returned2034. - The POST/api/pipelines/{uuid}/env/stop command calls the environmental teardown in a pipeline. If the data processing stages are still running when this endpoint is called, this endpoint returns an error. In other words, call ‘POST . . . /data/stop’ before calling this endpoint. An example response (202 Accepted):
{ “pipelineId”:“038bf27f-52f0-40cf-95db-b70b83ade772”, “data”:null, “statusCode”:202 } - The command is received2061 and data is obtained from the
data store 2062. If the pipeline exists2063 in the data base, a request accepted is returned2064; otherwise areject 404 pipeline not found is returned2034. - The command is received2071 and a test if an active pipeline exists2072 in Flink. If the pipeline is not active, and already running rejection is returned2100; otherwise a test to determine if the pipeline exists2073 is performed. If the pipeline is not in the database a
reject 404 pipeline not found is returned2074. If the pipeline exists in the database, the pipeline is deserialized2075. A test to determine if the operation was asuccess 2076 is performed and if not, a rejection ENV is not in a proper state is returned2077. If a success was detected, a request to Flink is made2078 and a status of the request is tested2079a, If the status is good, the accepted work is returned2079b; otherwise a Reject Flink rejects pipeline state is returned2079c. - The POST/api/pipelines/{uuid}/start/all starts both the environmental and the data processing stages in a pipeline. Starts the pipeline from the most recent save point, if one exists. The command uses payload parameters: ‘taskmanager-heap-mb’—the amount of heap to allocate to each task manger; ‘jobmanager-heap-mb’—the amount of heap to allocate to each job manager. Number of job managers is one; ‘taskmanager-slots’—the number of slots to allocate per taskmanager; ‘taskmanager-cpu-count’—the number of cpu cores to allocate per task manager; ‘jobmanager-cpu-count’—the number of cpu cores to allocate to the job manager; ‘job-parallelism’—the number of parallel instances to run at once; (optional) ‘job-checkpoint-timeout-seconds’—(default:600) the number of seconds before checkpoints or savepoint is considered failed; (optional) ‘job-checkpoint-pause-seconds’—(default:30) the number of seconds to wait before starting another checkpoint after a checkpoint completes; and (optional) ‘job-checkpoint-frequency-seconds’—(default:60) the interval in seconds by which checkpoints should occur. The command returns a200 (OK) status instead of a202 (Accepted) because Flink's API returns a200 when submitting a job. An example response (200 OK):
{ “pipelineId”:“038bf27f-52f0-40cf-95db-b70b83ade772”, “data”:null, “statusCode”:200 } - The GET/api/pipelines/{uuid}/data/status2081 returns the status of the data processing stages, by first determining if the pipeline exists in
Flink 2082, following up with a check for the pipeline in the database if the pipeline does not exist inFlink 2083. If it does exist in the database, a “pipeline never started” status may be returned2084, while if the pipeline does not exist in the database, a “404 pipeline not found”2074 error may be returned. If, however, the pipeline does exist inFlink 2082, the Flink status of the pipeline is fetched2085 and returned2086. An example response (200 OK): { “pipelineId”:“038bf27f-52f0-40cf-95db-b70b83ade772”, “data”:“RUNNING”, “statusCode”:200 } - The POST/api/pipelines/{uuid}/data/stop command stops the data processing stages in a pipeline (i.e., calls Flink with a save point). Returns an error if the pipeline does not have data processing stages. The command uses request parameter ‘graceful’ (optional): indicates whether to stop the pipeline with a save point. Acceptable values: ‘true’, ‘false’ (defaults to ‘true’). An example response (202 Accepted):
{ “pipelineId”:“038bf27f-52f0-40cf-95db-b70b83ade772”, “data”:null, “statusCode”:202 } - The POST/api/pipelines/{uuid}/stop/all stops the data processing stages in a pipeline (i.e., calls Flink with a save point). Returns an error if the pipeline does not have data processing stages. The command uses request parameter ‘graceful’ (optional): indicates whether to stop the pipeline with a save point. Acceptable values: ‘true’, ‘false’ (defaults to ‘true’). An example response (202 Accepted):
{ “pipelineId”:“038bf27f-52fb-40cf-95db-b70b83ade772”, “data”:null, “statusCode”:202 } - In both of the above stop commands, command is received2091 and a test2092 determines if the pipeline exists in Flink. If is exists, a request to Flink2095 is made and the Flink results are returned2096; otherwise a reject Pipeline ID is not running is returned2094.
FIG. 20D Illustrate the operation of Delete2003 and Put commands. The DELETE /api/pipelines/{uuid}2003 deletes the pipeline from the database. Does not stop the pipeline, so it's expected that the user calls ‘GET . . . /stop’ first. Calls to delete the pipeline while it is already active will be rejected. The command is received2301 and data is retrieved from thedata store 2302.Test 2303 determines if the pipeline exists in the database. If not aReject 404 pipeline not found is returned; otherwise test2305 determines if the pipeline is active in Flink. If not, aReject 404 pipeline not found is also returned2304; otherwise the pipeline is deserialized2306 and a stop Env function is called2307.Test 2308 determines whether the teardown was successful. If so, an update to the pipeline is made to indicate anew state 2309; otherwise remediation may be initiated2310.- The PUT/api/pipelines command2004 is a command having a content type: ‘application/json.’ This command updates the pipeline in the database, but does not start or stop the pipeline. A pipeline with no environmental stages and no data processing stages is considered invalid. The command uses payload fields: ‘pipeline’ (required): See [‘POST/api/pipelines’](#post-apipipelines) uuid of pipeline to update must be in the payload. An Example response (200 OK):
{ “pipelineId”:“038bf27f-52fb-40cf-95db-b70b83ade772”, “data”:“Pipeline updated”, “statusCode”:200 } - The command is received2401 and data is retrieved from the
data store 2402.Test 2403 determines if the pipeline exists in the database. If not aReject 404 pipeline not found is returned2404; otherwise test2405 determines if the ENV has not been started. If it has not been started, a Reject cannot update pipeline not active is returned2408; otherwise the pipeline is inserted into thedatabase 2406 and a success indication is returned2407. - The skilled person will be aware of a range of possible modifications of the various embodiments described above. Accordingly, the present invention is defined by the claims and their equivalents.
Claims (10)
1. A system for management and tracking of collaborative projects, comprising:
an automated planning service comprising a memory, a processor, and a plurality of programming instructions stored in the memory thereof and operable on the processor thereof, wherein the programmable instructions, when operating on the processor, cause the processor to:
operate a plurality of master nodes, each master node in turn operating a plurality of worker nodes;
receive a plurality of simulation conditions at a master node;
construct a plurality of simulation components based on the received simulation conditions;
construct a planning model based on the received simulation conditions;
assign, using a master node, a plurality of discrete simulation tasks to a plurality of worker nodes, each of the plurality of discrete simulation tasks being based on the constructed simulation components and planning model, wherein each of the plurality of worker nodes is assigned exactly one of the plurality of discrete simulation tasks at any given time during operation;
analyze results of each of the plurality of discrete simulation tasks as they are completed; and
provide the analyzed results as output.
2. The system ofclaim 1 , wherein the simulation conditions are retrieved from a data storage.
3. The system ofclaim 1 , wherein the simulation conditions are received via a RESTful API.
4. The system ofclaim 1 , wherein the analysis comprises determining an uncertainty value for at least a portion of the results.
5. The system ofclaim 1 , wherein the analysis comprises determining whether a plurality of end conditions have been met, and if the plurality of end conditions have been met, halting the assignment of discrete simulation tasks to worker nodes.
6. A method for management and tracking of collaborative projects, comprising the steps of:
operating, at an automated planning service, a plurality of master nodes, each master node in turn operating a plurality of worker nodes;
receiving a plurality of simulation conditions at a master node;
constructing a plurality of simulation components based on the received simulation conditions;
constructing a planning model based on the received simulation conditions;
assigning, using a master node, a plurality of discrete simulation tasks to a plurality of worker nodes, each of the plurality of discrete simulation tasks being based on the constructed simulation components and planning model, wherein each of the plurality of worker nodes is assigned exactly one of the plurality of discrete simulation tasks at any given time during operation;
analyzing results of each of the plurality of discrete simulation tasks as they are completed; and
providing the analyzed results as output.
7. The method ofclaim 6 , wherein the simulation conditions are retrieved from a data storage.
8. The method ofclaim 6 , wherein the simulation conditions are received via a RESTful API.
9. The method ofclaim 6 , wherein the analysis comprises determining an uncertainty value for at least a portion of the results.
10. The method ofclaim 6 , wherein the analysis comprises determining whether a plurality of end conditions have been met, and if the plurality of end conditions have been met, halting the assignment of discrete simulation tasks to worker nodes.
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