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Abstract
Though processing time-dependent data has been investigated for a long time, the research on temporal and especially stream reasoning over linked open data and ontologies is reaching its high point these days. In this tutorial, we give an overview of state-of-the art query languages and engines for temporal and stream reasoning. On a more detailed level, we discuss the new language STARQL (Reasoning-based Query Language for Streaming and Temporal ontology Access). STARQL is designed as an expressive and flexible stream query framework that offers the possibility to embed different (temporal) description logics as filter query languages over ontologies, and hence it can be used within the OBDA paradigm (Ontology Based Data Access in the classical sense) and within the ABDEO paradigm (Accessing Big Data over Expressive Ontologies).
This work has been supported by the European Commission as part of the FP7 project Optiquehttp://www.optique-project.eu/
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Institute for Softwaresystems (STS), Hamburg University of Technology, Hamburg, Germany
Özgür Lütfü Özçep & Ralf Möller
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National and Kapodistrian University of Athens, Greece
Manolis Koubarakis
School of Electrical and Computer Engineering, National Technical University of Athens, Zografou Campus, 15780, Zografou, Greece
Giorgos Stamou
School of Electrical and Computer Engineering, National Technical University of Athens, Zografou, Greece
Giorgos Stoilos
Department of Computer Science, University of Oxford, Oxford, UK
Ian Horrocks
University of California, Santa Cruz, CA, USA
Phokion Kolaitis
Institute for Informatics, University of Freiburg, Freiburg, Germany
Georg Lausen
Max-Planck Institute for Informatics, Saarbrücken, Germany
Gerhard Weikum
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Özçep, Ö.L., Möller, R. (2014). Ontology Based Data Access on Temporal and Streaming Data. In: Koubarakis, M.,et al. Reasoning Web. Reasoning on the Web in the Big Data Era. Reasoning Web 2014. Lecture Notes in Computer Science, vol 8714. Springer, Cham. https://doi.org/10.1007/978-3-319-10587-1_7
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