Detailed Description
In order to enable those skilled in the art to better understand the technical solutions of the present disclosure, the technical solutions of the embodiments of the present disclosure will be clearly and completely described below with reference to the accompanying drawings.
It should be noted that the terms "first," "second," and the like in the description and claims of the present disclosure and in the foregoing figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the disclosure described herein may be capable of operation in sequences other than those illustrated or described herein. The implementations described in the following exemplary examples are not representative of all implementations consistent with the present disclosure. Rather, they are merely examples of apparatus and methods consistent with some aspects of the present disclosure as detailed in the accompanying claims.
It should be further noted that, the user information (including, but not limited to, user equipment information, user personal information, etc.) and the data (including, but not limited to, data for presentation, analyzed data, etc.) related to the present disclosure are information and data authorized by the user or sufficiently authorized by each party.
The table generation method provided by the disclosure can be applied to an application environment as shown in fig. 1. Wherein the terminal 101 interacts with the server 102 via a network. Specifically, the terminal 101 may obtain, from a database of the server 102, aggregated data according to a preset aggregation dimension, where the aggregated data includes dimension data corresponding to the aggregation dimension, and index data corresponding to the index dimension, where the aggregation dimension includes a first aggregation dimension and a second aggregation dimension, the first aggregation dimension may be used to generate first header data of a table, and meanwhile, the aggregated data may represent a data correspondence between the index data and the first dimension data and between the index data and the second dimension data, and the terminal 101 may aggregate, according to the data correspondence, the second dimension data and the index dimension to generate second header data, and finally may generate a complete table by using the first header data, the second header data, and the data correspondence. The terminal 101 may be, but not limited to, various personal computers, notebook computers, smartphones, tablet computers, and portable wearable devices, and the server 102 may be implemented by a stand-alone server or a server cluster formed by a plurality of servers.
Fig. 2 is a flowchart illustrating a table generating method according to an exemplary embodiment, which is used in the terminal 101 as shown in fig. 2, and includes the following steps.
In step S201, aggregation data of a table to be generated is acquired, the aggregation data is obtained based on preset aggregation dimensions, the aggregation data comprises dimension data corresponding to the aggregation dimensions and index data corresponding to the index dimensions, the dimension data comprises first dimension data corresponding to a first aggregation dimension and second dimension data corresponding to a second aggregation dimension, and the first aggregation dimension is used for representing first header data of the table to be generated.
The to-be-generated table refers to an intersecting table to be generated, and the aggregated data refers to data that is aggregated in advance and is used for generating the to-be-generated table, where the aggregated data may be provided by the server 102, and the server 102 may be based on a preset aggregated dimension, where the aggregated dimension may be preset by a user of the terminal 101, and the detail data stored in the server 102 is aggregated, so as to obtain the aggregated data. For example, the server 102 may store the results of all students in each class in a plurality of schools, and the user may aggregate the results of all students by using the schools and the classes as an aggregation dimension, to obtain an average score or a highest score of each class in each school as aggregation data. Meanwhile, the aggregate data may be composed of two parts, namely dimension data corresponding to the characteristic aggregate dimension and index data representing the index dimension. For example, a certain piece of aggregated data may be school a-class a-average score a, and in the aggregated data, the aggregated dimension is class a, the dimension data corresponding to the aggregated dimension may be class a, the index dimension may be average score a, and the index data may be average score a. Each piece of aggregated data may be composed of dimension data and index data.
Meanwhile, the aggregation dimension may further include a first aggregation dimension and a second aggregation dimension, wherein dimension data corresponding to the first aggregation dimension may be used as first dimension data, dimension data corresponding to the second aggregation dimension may be used as second dimension data, and the first aggregation dimension and the second aggregation dimension may be selected from aggregation dimensions according to actual needs by a user. And the first aggregation dimension is a dimension for generating the first header data in the intersecting table, and the dimension does not need header intersection, so that the first aggregation dimension can be directly used as the first header data, namely, the header of the common data column in the intersecting table, and the data column of the header is used for displaying the first dimension data. In addition, in special cases, all aggregation dimensions may be used as the second aggregation dimension in the cross table to be generated.
In step S202, according to the aggregated data, data correspondence between the first dimension data, the second dimension data, and the index data is generated, and the second dimension data and the index dimension are aggregated, so as to generate second header data of the table to be generated.
The second header data refers to a header in an intersecting data column in the intersecting table, wherein the header is required to be placed with the possibility of value of the second aggregate dimension for intersecting, and the header data column is used for displaying the index data, so that the second header data is composed of two parts, namely dimension data corresponding to the second aggregate dimension, namely second dimension data, and index dimension of the index data.
Specifically, after obtaining the aggregate data, the terminal 101 may obtain, according to the aggregate data, a correspondence between each index data and the first dimension data and the second dimension data. For example, taking a school dimension as a first aggregation dimension and a class dimension as a second aggregation dimension as an example, for school a-class a-average score a, a correspondence relationship between index data average score a and first dimension data school a and second dimension data class a may be established, and for school a-class B-average score B, a correspondence relationship between index data average score B and first dimension data school a and second dimension data class B may be established, and terminal 101 may further aggregate the second dimension data and the index dimension, thereby generating second header data, for example, aggregate class a and average score dimension, generate second header data a, aggregate class B and average score dimension, generate second header data B, and so on.
In step S203, a complete table corresponding to the table to be generated is generated according to the first header data, the second header data and the data correspondence.
Finally, after the first header data and the second header data of the table to be generated are determined, the index data can be further integrated by utilizing the data corresponding relation between the index data and the first dimension data and the second dimension data, so that a complete table is generated.
For example, as shown in table 1, table 1 is an aggregated data lookup table composed of aggregated data obtained by the terminal 101 in one embodiment, where the aggregated data is aggregated by a date dimension, a place dimension, and a sponsor character dimension as aggregated dimensions, and the user number dimension and the active time dimension may be index dimensions, where the date dimension includes dimension data, i.e., date a and date B, the place dimension includes dimension data, i.e., place a and place B, and the sponsor character dimension includes dimension data, i.e., sponsor character dimension includes character a, character B, and character C. The user number dimension and the active time dimension are used as index dimensions, the index data of the user number dimension can comprise user number A, user number B, user number C, user number D and user number E, and the index data of the active time dimension can comprise active time A, active time B, active time C, active time D and active time E.
Table 1 aggregate data lookup table
| Date dimension | Location dimension | Dimension of underwriting person | User number dimension | Active duration dimension |
| Date A | Site A | Character A | User number A | Active duration A |
| Date A | Site A | Character B | User number B | Active duration B |
| Date B | Site A | Character A | Number of users C | Active duration C |
| Date B | Site A | Character B | User number D | Active duration D |
| Date B | Site B | Character C | User number E | Active duration E |
If the date dimension is the first aggregation dimension and the place dimension and the sponsor dimension are the second aggregation dimension, the form of the generated table to be generated may be as shown in table 2. It can be seen that, in table 2, the date dimension is taken as the first header data, and the date a and the date B are arranged in the data column corresponding to the first header data, and the second header data may include a plurality of pieces of second dimension data and index dimensions, which are respectively the location B-person C-user dimension, the location B-person C-active duration dimension, the location a-person a-user dimension, the location a-person a-active duration dimension, the location a-person B-user dimension, and the location a-person B-active duration dimension. Meanwhile, the data column corresponding to the second header data may be arranged with the index data, that is, the number of users a, B, C, D, and E, and the active duration a, the active duration B, the active duration C, the active duration D, and the active duration E.
TABLE 2 forms to be generated
The method for generating the table comprises the steps of obtaining aggregated data of the table to be generated, aggregating the aggregated data based on preset aggregated dimensions, wherein the aggregated data comprise dimension data corresponding to the aggregated dimensions and index data corresponding to the index dimensions, the dimension data comprise first dimension data corresponding to the first aggregated dimensions and second dimension data corresponding to the second aggregated dimensions, the first aggregated dimensions are used for representing first header data of the table to be generated, according to the aggregated data, data corresponding relations among the first dimension data, the second dimension data and the index data are generated, aggregation processing is conducted on the second dimension data and the index dimensions, second header data of the table to be generated are generated, and a complete table corresponding to the table to be generated is generated according to the first header data, the second header data and the data corresponding relations. According to the method and the device, the aggregation data used for generating the table to be generated are obtained through aggregation based on preset aggregation dimensions, wherein the aggregation dimensions comprise a first aggregation dimension and a second aggregation dimension, the first aggregation dimension can be used for generating the first header data, the second dimension data contained in the second aggregation dimension can be subjected to aggregation processing with the index dimension to obtain the second header data, and therefore the data corresponding relation between the index data and the first dimension data and the data corresponding relation between the index data and the second dimension data can be utilized, the generated first header data and the generated second header data can be utilized to obtain a final complete table.
In an exemplary embodiment, as shown in fig. 3, in step S202, the method may further include:
in step S301, an intermediate node of the aggregation tree is determined from the second dimension data, and leaf nodes of the aggregation tree are determined from the index dimension.
Wherein, the aggregation tree refers to a tree structure formed by aggregation data, and the tree structure is composed of root nodes, intermediate nodes and leaf nodes, wherein all nodes share the same root node. In this embodiment, the intermediate node of the aggregation tree may be composed of the second dimension data corresponding to each second aggregation dimension, and the leaf node may be determined by each index dimension, for example, for the location a-person a-user number a existing in the aggregation data shown in table 1, the location a and person a may be obtained as the intermediate node, the user number dimension may be used as the branch of the leaf node, for the location a-person a-active duration a, the location a and person a may be obtained as the intermediate node, the active duration dimension may be used as the branch of the leaf node, and for the aggregation data of the location a-person B-user number C, the location a and person B may be obtained as the intermediate node, and the user number dimension may be used as the branch of the leaf node. And determining a plurality of intermediate nodes and a plurality of leaf nodes of the aggregation tree by the second dimension data and the index dimension contained in the aggregation data.
In step S302, a tree structure of an aggregation tree is obtained according to the intermediate nodes and the leaf nodes;
In step S303, second header data is generated based on the tree structure.
After the terminal 101 obtains the intermediate nodes and the leaf nodes included in each branch in step S301, a connection relationship between the intermediate nodes and the leaf nodes of each branch may be established, so as to obtain a tree structure of the aggregation tree, and generate corresponding second header data according to the tree structure. For example, as shown in fig. 4, the tree structure of the aggregation tree generated by using the aggregation data shown in table 1 may include multiple branches, which are respectively a location B-person C-user number dimension, a location B-person C-active duration dimension, a location a-person a-user number dimension, a location a-person a-active duration dimension, a location a-person B-user number dimension, and a location a-person B-active duration dimension, and then the second header data generated by using the tree structure may also be adapted to the tree structure, that is, the second header data shown in table 2 may be generated.
In this embodiment, the second dimension data may be used to determine the intermediate node of the aggregation tree, and the dimension index may be used to determine the corresponding leaf node, so that the second header data may be obtained by using the tree structure of the aggregation tree obtained by the intermediate node and the leaf node, and because the second dimension data is only related to the value range corresponding to the second aggregation dimension, and the index dimension is relatively fixed, the data correspondence between the index data and the first dimension data and the second dimension data is not required to be established, and therefore, the process of establishing the second header data by the terminal 101 may be performed simultaneously with the process of executing data aggregation by the server 102, and the generation of the second header data is not required to be executed after the server 102 obtains the aggregation data, so that the efficiency of table generation may be further improved.
Further, the number of second polymerization dimensions is plural, and as shown in FIG. 5, step S301 may further include:
in step S501, an intermediate node hierarchy order corresponding to each second aggregation dimension is acquired.
In this embodiment, the number of the second aggregation dimensions may be plural, for example, when the second aggregation dimensions may include a place dimension and a sponsor dimension, the intermediate node may also include plural levels, where one level a may be used to arrange the second dimension data corresponding to the place dimension, and another level B may be used to arrange the second dimension data corresponding to the sponsor dimension. The intermediate node hierarchy order is the hierarchy order of the above-mentioned each hierarchy, for example, the intermediate nodes may be ordered according to the hierarchy a-hierarchy B, where the second dimension data corresponding to the place dimension is arranged at the upper layer first, and then the second dimension data corresponding to the underwriting person dimension is arranged at the lower layer. The order may be according to the level B-level a, where the second dimension data corresponding to the dimension of the sponsor character is arranged at the upper layer, and then the second dimension data corresponding to the dimension of the place is arranged at the lower layer. The intermediate node hierarchy order may be set in the terminal 101 in advance or may be set by the user as needed.
In step S502, the corresponding second-dimension data of each second aggregation dimension are arranged and combined according to the intermediate node hierarchy order, to obtain a plurality of second-dimension data combinations.
The second dimension data combination is a combination of dimension data obtained after the second dimension data is ordered and combined according to the intermediate node hierarchy, for example, when the intermediate node hierarchy order is ordered for the hierarchy a-hierarchy B, because the order represents the second dimension data corresponding to the place dimension arranged at the upper layer first, and then the second dimension data corresponding to the person dimension arranged at the lower layer, the generated second dimension data combination may be a combination of the place a-person a, the place a-person B, or the like, and when the intermediate node hierarchy order is ordered for the hierarchy B-hierarchy a, because the order represents the second dimension data corresponding to the person dimension arranged at the upper layer first, and then the second dimension data corresponding to the place dimension arranged at the lower layer first, the generated second dimension data combination may be a-place a, the person B-place a, or the like.
In step S503, an intermediate node is generated from the plurality of second dimensional data combinations.
After all the second-dimension data combinations are determined, the second-dimension data combinations can be integrated, so that an intermediate node of the aggregation tree is obtained.
In this embodiment, the user may set the hierarchical order of the intermediate nodes, arrange and combine the second dimension data to obtain the second dimension data combination, and since the second dimension data combination may be used to generate the intermediate nodes and the intermediate nodes are used to generate the second header data, the hierarchical order of each second dimension combination in the second header data may be adapted to the hierarchical order of the intermediate nodes, i.e. the user may change the arrangement mode of the second header data by setting the hierarchical order of the intermediate nodes, thereby further meeting the user's requirement on the custom ordering of the table.
Further, the step S503 may further include combining the second-dimension data with the same content in the plurality of second-dimension data combinations, and arranging the second-dimension data with different content according to a preset sequence to generate an intermediate node.
The preset sequence refers to an arrangement sequence of a second dimension sequence set in advance for a plurality of second dimension data of the same second polymerization dimension, for example, for a location a and a location B of dimension data included in a location dimension, where the arrangement sequence may be before the location a is placed at the location B or before the location B is placed at the location a, or may be set by a user according to actual needs. Specifically, after each second-dimension data combination is obtained, the second-dimension data included in each second-dimension data combination can be determined, if second-dimension data with the same content exists, the same second-dimension data are combined, and if the content is not the same, the second-dimension data can be arranged according to the set arrangement sequence, so that a final intermediate node is obtained.
For example, as shown in FIG. 6, the second dimension data combination obtained in step S502 may include a location A-persona A, a location A-persona B, and a location B-persona C, where the preset order may be that the ordering of location B is located before location A and the ordering of persona A is located before persona B, then after obtaining location A-persona A, an intermediate node in the first branch may be sequentially generated, and the user dimension and the active duration dimension may be respectively connected as leaf nodes to the intermediate node as shown in part 601 of FIG. 6. The terminal 101 may then obtain the location a-person B, where the location a may be combined because the location a is the same as the middle node of the location a in the first branch, and meanwhile, because the order of the person a is located before the person B, the task B may be arranged after the middle node of the person a in the first branch to form a second branch, and meanwhile, the user dimension and the active duration dimension are respectively connected as leaf nodes to the middle node of the second branch, as shown in part 602 in fig. 6. And finally, the terminal 101 obtains the site B-person C, which belongs to second dimension data with different contents, and since the arrangement sequence of the site B is before the site a, the sequence of the branches can be set before the branch corresponding to the site a, and the user dimension and the active duration dimension are respectively connected as leaf nodes, thereby forming the tree structure shown in part 603 in fig. 6.
In this embodiment, the intermediate nodes of the aggregation tree are generated by combining the same content and arranging the different content in sequence, and the process supports concurrent processing, so that the method is applicable to application scenarios of clustering processing, and the table generation efficiency is further improved.
In addition, as shown in fig. 7, after step S204, the method may further include:
in step S701, table location information matched with the data query request is acquired in response to the data query request.
The data query request is a query request for querying first dimension data and second dimension data matched with a certain index data, when a user views a generated complete table, a clicking mode can be performed on a table position displayed in the certain table, the data query request for the index data indicated by the table position is triggered, at this time, the terminal 101 can respond to the request, so as to determine table position information matched with the request, for example, row coordinate information and column coordinate information of a grid corresponding to the trigger request.
In step S702, target leaf node ranking information matched by the data query request is acquired based on the table position information.
In this embodiment, the terminal 101 establishes a correspondence between table position information and leaf node ordering information in advance, and may establish a correspondence between a column position of a table and leaf node ordering information in advance, and since the second header data is generated based on a tree structure in this embodiment, the order of the second header data may be adapted to the ordering of leaf nodes, and the tree structure in table 2 and fig. 4 is taken as an example, the second header data represented by the location a-character a-active length dimension is taken as a fifth column, and at the same time the column is in a fourth order in the tree structure in fig. 4, and the second header data represented by the location a-character B-user length dimension is in a sixth column, and at the same time the column is in a fifth order in the tree structure in fig. 4, and it can be seen that in table 2, by subtracting the table position information, the corresponding leaf node ordering information may be obtained. In this embodiment, after the table position information is obtained, the target leaf node ordering information corresponding to the query request may be obtained according to the conversion relationship between the table position information and the node ordering information.
In step S703, target index data associated with the target leaf node ranking information is determined based on the association relationship between each index data and the leaf node ranking information established in advance.
Meanwhile, the present embodiment further establishes an association relationship between each index data and the leaf node ordering information, for example, as shown in fig. 8, when an aggregation tree is constructed, the left and right leaf nodes of the aggregation tree are associated through an array to form an auxiliary index, so that when the target leaf node ordering information is obtained, a corresponding array can be found by querying the auxiliary index, and the array can be used for representing a storage position of the index data, so that the corresponding index data can be obtained as target index data according to the association relationship.
In step S704, first dimension data and second dimension data corresponding to the target index data are acquired according to the data correspondence.
Finally, after the target index data is determined, corresponding first dimension data and second dimension data can be inquired according to the pre-constructed data corresponding relation.
In this embodiment, the query of the data may be assisted by establishing an association relationship between each index data and the leaf node ordering information, that is, by establishing an auxiliary index, so that the efficiency of the data query may be improved.
In an exemplary embodiment, as shown in fig. 9, step S204 may further include:
in step S901, a first header data column of the complete table is generated using the first header data and the first dimension data corresponding to the first aggregation dimension.
The first header data column is a data column taking the first header data as a header, and since the first header data represents a first aggregation dimension, the data in the first header data column may be arranged by first dimension data corresponding to the first aggregation dimension, for example, in a table shown in table 2, a date dimension may be taken as the first header data, and the first header data column is formed by first dimension data included in a date dimension, that is, a date a and a date B.
In step S902, second header data corresponding to each index data is determined according to the data correspondence relationship.
The second header data is composed of the second dimension data and the index dimension, and the corresponding relation between each index data and the second dimension data is recorded in the corresponding relation of the data, so that the second header data can be obtained according to the corresponding relation and the index dimension of the index data.
In step S903, a second header data column of the complete table is generated by using each index data and the second header data corresponding to each index data, where the number of rows of each index data in the second header data column is the same as the number of rows of the first dimension data corresponding to each index data in the first header data column.
After the second header data corresponding to each index data is determined, the index data can be further filled into the corresponding position of the table to be generated by utilizing the data corresponding relation, so that the complete table is generated. For example, taking table 2 as an example, regarding the index data user number a, the corresponding second header data is the location a-person a-user number dimension, and the correspondence between the user number a and the date a is stored in the data correspondence, so when the second header data column is generated, the user number a may be set in the data column of the second header data represented by the location a-person a-user number dimension, and since the user number a has the correspondence with the date a, the number of lines of the user number a in the second header data column may be adapted to the number of lines of the date a in the first header data column. For the user number E, the corresponding second header data is the location B-character C-user number dimension, and meanwhile, since the user number E has a corresponding relationship with the date B, the number of rows of the user number E in the second header data column can be adapted to the number of rows of the date B in the first header data column. By the above method, the index data and the second header data contained in the aggregate data can be utilized to generate the second header data column of the complete table.
In step S904, a complete table is obtained according to the first header data column and the second header data column.
Finally, after the first header data column is obtained in step S901 and the second header data column is obtained in step S903, the first header data column and the second header data column may be integrated, so as to obtain a complete table.
In this embodiment, after the data correspondence relationship and the first header data and the second header data are obtained, the first header data and the second header data may be used to generate a corresponding first header data column and a corresponding second header data column, and further the first header data column and the second header data column are used to generate a complete table, so that accuracy of the generated complete table may be improved.
Further, step S902 may further include obtaining second dimension data corresponding to each index data according to the data correspondence, and determining second header data corresponding to each index data based on the second dimension data corresponding to each index data and the index dimension corresponding to each index data.
Specifically, since the data correspondence may store the correspondence between all the index data and the second dimension data, the second dimension data corresponding to each index data may be obtained according to the correspondence, for example, for the user number a, the second dimension data corresponding to the user number a is the place a and the person a, and for the user number E, the second dimension data corresponding to the user number E is the place B and the person C, and by the above method, the second dimension data corresponding to each index data may be obtained. Meanwhile, the second dimension data corresponding to each index data and the index dimension corresponding to each index data can be integrated, so that the second header data of each index data is obtained, for example, the corresponding index dimension of the user number A is the user number dimension, so that the corresponding second header data is the place A-character A-user number dimension, and the corresponding index dimension of the user number E is the user number dimension, so that the corresponding second header data is the place B-character C-user number dimension.
In this embodiment, the terminal 101 may further determine the corresponding second header data according to the second dimension data corresponding to each index data and the index dimension corresponding to each index data, so that the efficiency of obtaining the second header data may be improved.
In an exemplary embodiment, a method for generating a cross table is further provided, where the method firstly obtains dimension aggregation query data through a data query service, then generates cross headers from the dimension aggregation data based on a merged tree structure, and finally combines the cross headers with the headers to form a complete cross table, as shown in fig. 10, and may include the following 5 main steps:
1. Data inquiry, namely aggregating original detail data according to granularity to be used through SQL aggregation inquiry, and narrowing the data processing range to obtain dimension aggregation inquiry data (aggregation data for short);
2. Preprocessing data, namely classifying aggregate data according to common dimension, cross dimension and index column, generating ValueMap, and recording the corresponding relation between indexes and dimension;
3. Parallelization cross header processing, namely, utilizing the characteristics of a merging tree to aggregate cross dimension and index into a complete cross header, wherein the process supports parallelization processing;
4. Form data integration, namely integrating the common dimension, the cross header and ValueMap to form a complete cross table structure;
5. And generating an auxiliary index, namely associating left and right leaf nodes (i.e. index columns) of MERGETREE through a linked list to form the auxiliary index so as to traverse any aggregation dimension of the cross table for the chart intercommunication scene.
The specific flow of each step is as follows:
(1) Data query
In this embodiment, the data used to produce the cross-table is not based on the original detail data, but is first aggregated by an SQL aggregate query, with the original detail data being aggregated according to the granularity to be used. Doing so may narrow the data processing, such as where the detail data may be hundreds of thousands or millions, but after the dimension set, the magnitude of the aggregate data result tends to drop by several orders of magnitude. On the other hand, the query capability of the database can be fully exerted, and the common database has special performance optimization on dimension set calculation, so that the dimension set calculation is submitted to the database for processing, and the best can be achieved.
(2) Data preprocessing
After the aggregate data is obtained, data preprocessing is required. This step classifies the aggregate data and generates ValueMap to record the correspondence between the index and the dimension. The basis of the classification is as follows:
"common dimension", i.e., columns that do not require dimension interleaving;
The cross dimension is a column which needs to place the value possibility to the header for cross;
The index column, i.e., the numerical column, is the main part of the cross table;
The sequence of the aggregate data columns is adjusted first, and the aggregate data columns are guaranteed to be stored in the sequence of the common dimension, the cross dimension and the index columns. Then, the combination of the values of the common dimension and the cross dimension of each row of data is taken as a key, the value of each index column is respectively a value, and 'ValueMap' is constructed and stored in the memory. ValueMap are used to generate the table body of the cross table.
(3) Parallelization header processing
The step utilizes the characteristic of the merging tree to aggregate the cross dimension and the index into a complete cross header, and the process supports parallelization processing. For convenience of description, the procedure of the following processing will be described first in terms of a case of progressive serial processing.
The merging tree characteristics utilized are mainly two:
1. The same item is combined, nodes with the same content are automatically combined, and sub-nodes are shared;
2. Different items are arranged, parallel brother nodes are created for the nodes with different contents, and the arrangement sequence of the brother nodes can be customized.
Taking the aggregated data in the table 1 as an example, traversing each row of data, taking the cross dimension as an intermediate path node of the aggregated tree, taking the dimension column as a leaf node, wherein the cross dimension of the first row of data is a position A-character A, performing layer-by-layer depth first insertion, then parallelly inserting two dimension columns of the user number and the active time length as the leaf node, the cross dimension of the second row of data is a position A-character B, merging the same item of position A and sharing the child node character A and the character B, the leaf nodes are all the same, no operation is needed, the cross dimension of the third row of data is a position A-character A, all the same item of node exists, the merging tree content is unchanged, the cross dimension of the fourth row of data is a position A-character B, all the existing nodes are unchanged, and the merging tree content is unchanged. The cross dimension of the fifth line data is a position B-character C, all the fifth line data are newly appeared contents, new brother nodes are sequentially created, the position B is higher than the position A in the sequence of the position B in the sequence rule, the position B is arranged on the left, if the sequence rule is not specified, the position B is arranged according to the appearance sequence of the contents, and the cross table head based on the merging tree is produced.
It should be noted that the merging tree supports merging of multiple subtrees into one tree. Therefore, in the big data scene, the processing procedures can be processed simultaneously to form a plurality of sub-merging trees, and finally, a full merging tree is formed. Such a feature allows the solution to be easily extended to clustered processing.
(4) Form data integration
After the cross header is produced, the common dimensions, cross header and ValueMap need to be data integrated to form a complete cross table. The method comprises the steps of acquiring common dimensions of aggregated data row by row, performing depth-first traversal on a merging tree of a cross table head, and finding out a corresponding index value in ValueMap by combining the acquired common dimensions and a combined Key produced by merging leaf child node paths. For example, for the first row of the aggregated data, the common dimension is the date a, the first leaf node path of the merged tree is the location a-person a-user number dimension, then the joint dimension key is the date a-location a-person a, and the index value corresponding to the user number dimension in valueMap is the user number a. And repeating the steps to obtain the numerical content in each cell of the cross header.
(5) Generating auxiliary index
In order to better adapt to the interaction scene of the chart, the cross table is required to support efficient access of the related dimension information of the numerical value so as to interact with other charts. If the value of the active duration a in table 2 is selected (the second last row, the fifth column), the associated dimension information needs to be extracted, i.e. the date is the date a, the place is the place a, the underwriting person is the person a, etc. This requires the tree path of any leaf node to be found quickly within the merge tree, which is cost prohibitive to traverse the leaf node from the root node trigger depth of the merge tree each time, and thus requires the construction of an auxiliary index to solve this problem.
When the merging tree is built, the upper nodes and the lower nodes are linked through the double linked list, so that the leaf nodes can be easily queried reversely to the root node, and furthermore, the left and right leaf nodes (i.e. index columns) of the merging tree are related through an array to form an auxiliary index, so that the leaf nodes of any column can be quickly positioned on the merging tree. By the aid of the two indexes, the merging tree in the embodiment is more efficient and flexible in a chart intercommunication scene.
According to the embodiment, when the cross table is generated, data aggregation query can be performed, the computing capability of a big data technology is fully utilized, massive computing summarization is supported, concurrent processing is supported by a frame by utilizing the characteristic of the merge table, clustering expansion is easy, custom ordering rules are supported by the merge tree table head, the generated cross table head is more flexible, meanwhile, the cross table is combined with an auxiliary index, dimension traversal is more flexible, and a multi-chart interaction scene is supported.
It should be understood that, although the steps in the flowcharts of this disclosure are shown in order as indicated by the arrows, these steps are not necessarily performed in order as indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least a portion of the steps in the figures may include steps or stages that are not necessarily performed at the same time, but may be performed at different times, nor does the order in which the steps or stages are performed necessarily performed in sequence, but may be performed alternately or alternately with other steps or at least a portion of the steps or stages in other steps.
It should be understood that the same/similar parts of the embodiments of the method described above in this specification may be referred to each other, and each embodiment focuses on differences from other embodiments, and references to descriptions of other method embodiments are only needed.
Fig. 11 is a block diagram of a table generating apparatus according to an exemplary embodiment. Referring to fig. 11, the apparatus includes an aggregate data acquisition unit 1101, a second header generation unit 1102, and a complete table generation unit 1103.
The aggregation data acquisition unit 1101 is configured to perform acquisition of aggregation data of a form to be generated, wherein the aggregation data is obtained by aggregation based on preset aggregation dimensions and comprises dimension data corresponding to the aggregation dimensions and index data corresponding to the index dimensions, the dimension data comprises first dimension data corresponding to a first aggregation dimension and second dimension data corresponding to a second aggregation dimension, and the first aggregation dimension is used for representing first header data of the form to be generated;
A second header generation unit 1102 configured to perform data correspondence between the first dimension data, the second dimension data, and the index data according to the aggregated data, and aggregate the second dimension data and the index dimension to generate second header data of the table to be generated;
the complete table generating unit 1103 is configured to generate a complete table corresponding to the table to be generated according to the first header data, the second header data and the data correspondence.
In an exemplary embodiment, the second header generation unit 1102 is further configured to perform determining an intermediate node of the aggregation tree according to the second dimension data, determining a leaf node of the aggregation tree according to the index dimension, obtaining a tree structure of the aggregation tree according to the intermediate node and the leaf node, and generating the second header data based on the tree structure.
In an exemplary embodiment, the number of second polymer dimensions is a plurality, the second header generation unit 1102 is further configured to perform obtaining an intermediate node hierarchy order corresponding to each second polymer dimension, arrange and combine the corresponding second dimension data of each second polymer dimension according to the intermediate node hierarchy order to obtain a plurality of second dimension data combinations, and generate an intermediate node according to the plurality of second dimension data combinations.
In an exemplary embodiment, the second header generating unit 1102 is further configured to perform combining the second-dimension data with the same content in the plurality of second-dimension data combinations, and arrange the second-dimension data with different contents in a preset order, so as to generate the intermediate node.
In an exemplary embodiment, the table generating device further comprises a dimension data query unit configured to execute the step of responding to the data query request and obtaining table position information matched with the data query request, the step of obtaining target leaf node ordering information matched with the data query request based on the table position information, the step of determining target index data associated with the target leaf node ordering information based on the pre-established association relationship between each index data and the leaf node ordering information, and the step of obtaining first dimension data and second dimension data corresponding to the target index data according to the data corresponding relationship.
In an exemplary embodiment, the complete table generating unit 1103 is further configured to generate a first header data column of the complete table by using the first header data and the first dimension data corresponding to the first aggregate dimension, determine second header data corresponding to each index data according to the data correspondence, generate a second header data column of the complete table by using each index data and the second header data corresponding to each index data, wherein the number of rows of each index data in the second header data column is the same as the number of rows of the first dimension data corresponding to each index data in the first header data column, and obtain the complete table according to the first header data column and the second header data column.
In an exemplary embodiment, the complete table generating unit 1103 is further configured to obtain second dimension data corresponding to each index data according to the data correspondence, and determine second header data corresponding to each index data based on the second dimension data corresponding to each index data and the index dimension corresponding to each index data.
The specific manner in which the various modules perform the operations in the apparatus of the above embodiments have been described in detail in connection with the embodiments of the method, and will not be described in detail herein.
Fig. 12 is a block diagram illustrating an electronic device 1200 for table generation, according to an example embodiment. For example, the electronic device 1200 may be a mobile phone, computer, digital broadcast terminal, messaging device, game console, tablet device, medical device, exercise device, personal digital assistant, or the like.
Referring to FIG. 12, an electronic device 1200 can include one or more of a processing component 1202, a memory 1204, a power component 1206, a multimedia component 1208, an audio component 1210, an input/output (I/O) interface 1212, a sensor component 1214, and a communications component 1216.
The processing component 1202 generally controls overall operation of the electronic device 1200, such as operations associated with display, telephone calls, data communications, camera operations, and recording operations. The processing component 1202 may include one or more processors 1220 to execute instructions to perform all or part of the steps of the methods described above. Further, the processing component 1202 may include one or more modules that facilitate interactions between the processing component 1202 and other components. For example, the processing component 1202 may include a multimedia module to facilitate interaction between the multimedia component 1208 and the processing component 1202.
The memory 1204 is configured to store various types of data to support operations at the electronic device 1200. Examples of such data include instructions for any application or method operating on the electronic device 1200, contact data, phonebook data, messages, pictures, video, and so forth. The memory 1204 may be implemented by any type or combination of volatile or non-volatile memory devices, such as Static Random Access Memory (SRAM), electrically Erasable Programmable Read Only Memory (EEPROM), erasable Programmable Read Only Memory (EPROM), programmable Read Only Memory (PROM), read Only Memory (ROM), magnetic memory, flash memory, magnetic disk, optical disk, or graphene memory.
The power supply assembly 1206 provides power to the various components of the electronic device 1200. The power supply components 1206 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power for the electronic device 1200.
The multimedia component 1208 includes a screen between the electronic device 1200 and a user that provides an output interface. In some embodiments, the screen may include a Liquid Crystal Display (LCD) and a Touch Panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive input signals from a user. The touch panel includes one or more touch sensors to sense touches, swipes, and gestures on the touch panel. The touch sensor may sense not only the boundary of a touch or slide action, but also the duration and pressure associated with the touch or slide operation. In some embodiments, the multimedia component 1208 includes a front camera and/or a rear camera. When the electronic device 1200 is in an operational mode, such as a shooting mode or a video mode, the front camera and/or the rear camera may receive external multimedia data. Each front and rear camera may be a fixed optical lens system or have focal length and optical zoom capabilities.
The audio component 1210 is configured to output and/or input audio signals. For example, the audio component 1210 includes a Microphone (MIC) configured to receive external audio signals when the electronic device 1200 is in an operational mode, such as a call mode, a recording mode, and a speech recognition mode. The received audio signals may be further stored in the memory 1204 or transmitted via the communications component 1216. In some embodiments, the audio component 1210 further comprises a speaker for outputting audio signals.
The I/O interface 1212 provides an interface between the processing component 1202 and peripheral interface modules, which may be a keyboard, click wheel, buttons, etc. These buttons may include, but are not limited to, a home button, a volume button, an activate button, and a lock button.
The sensor assembly 1214 includes one or more sensors for providing status assessment of various aspects of the electronic device 1200. For example, the sensor assembly 1214 may detect an on/off state of the electronic device 1200, a relative positioning of the components, such as a display and keypad of the electronic device 1200, the sensor assembly 1214 may also detect a change in position of the electronic device 1200 or a component of the electronic device 1200, the presence or absence of a user's contact with the electronic device 1200, an orientation or acceleration/deceleration of the device 1200, and a change in temperature of the electronic device 1200. The sensor assembly 1214 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact. The sensor assembly 1214 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor assembly 1214 may also include an acceleration sensor, a gyroscopic sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.
The communications component 1216 is configured to facilitate communication between the electronic device 1200 and other devices, either wired or wireless. The electronic device 1200 may access a wireless network based on a communication standard, such as WiFi, an operator network (e.g., 2G, 3G, 4G, or 5G), or a combination thereof. In one exemplary embodiment, the communication component 1216 receives broadcast signals or broadcast-related information from an external broadcast management system via a broadcast channel. In one exemplary embodiment, the communications component 1216 further includes a Near Field Communication (NFC) module to facilitate short range communications. For example, the NFC module may be implemented based on Radio Frequency Identification (RFID) technology, infrared data association (IrDA) technology, ultra Wideband (UWB) technology, bluetooth (BT) technology, and other technologies.
In an exemplary embodiment, the electronic device 1200 may be implemented by one or more Application Specific Integrated Circuits (ASICs), digital Signal Processors (DSPs), digital Signal Processing Devices (DSPDs), programmable Logic Devices (PLDs), field Programmable Gate Arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic elements for executing the methods described above.
In an exemplary embodiment, a computer-readable storage medium is also provided, such as memory 1204, including instructions executable by processor 1220 of electronic device 1200 to perform the above-described method. For example, the computer readable storage medium may be ROM, random Access Memory (RAM), CD-ROM, magnetic tape, floppy disk, optical data storage device, etc.
In an exemplary embodiment, a computer program product is also provided, comprising instructions executable by the processor 1220 of the electronic device 1200 to perform the above method.
It should be noted that the descriptions of the foregoing apparatus, the electronic device, the computer readable storage medium, the computer program product, and the like according to the method embodiments may further include other implementations, and the specific implementation may refer to the descriptions of the related method embodiments and are not described herein in detail.
Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This disclosure is intended to cover any adaptations, uses, or adaptations of the disclosure following the general principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.
It is to be understood that the present disclosure is not limited to the precise arrangements and instrumentalities shown in the drawings, and that various modifications and changes may be effected without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.