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Constructing Event Processing Systems of Layered and Heterogeneous Events with SPARQL

  • Original Article
  • Published:
Journal on Data Semantics

Abstract

SPARQL was originally developed to process queries over finite-length datasets encoded as RDF graphs. Processing of infinite data streams can be enabled through continuous incremental evaluation of an incoming event stream. SPARQL Update provides tools for interconnecting queries, enabling event processing applications to be constructed out of multiple incrementally processed collaborating rules. These rule networks can perform event processing on heterogeneous event structures. Heterogeneous event support combined with the capability to synthesise new events enables the creation of layered event processing networks. In this paper, we review the different types of complex event processing building blocks presented in the literature and show their translations to SPARQL Update rules through examples, supporting a modular and layered approach. The interconnected examples demonstrate the creation of an elaborate network for solving event processing tasks. The performance of the example event processing network is verified on the Instans platform.

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Notes

  1. http://streamreasoning.org/download.

  2. http://code.google.com/p/cqels/.

  3. https://github.com/jpcik/morph-streams.

  4. https://www.w3.org/community/rsp/.

  5. http://code.google.com/p/etalis/.

  6. https://github.com/skomazec/Sparkweave.

  7. Incremental eNgine for STANding Sparql, http://instans.org/.

  8. http://www.complexevents.com/2016/05/12/cep-tooling-market-survey-2016/.

  9. ETALIS listed as discontinued.

  10. http://www.espertech.com/esper/.

  11. http://wso2.com/products/complex-event-processor/.

  12. http://ontologydesignpatterns.org/wiki/Submissions:EventProcessing.

  13. http://www.w3.org/TR/turtle/#sec-grammar.

  14. http://factforge.net.

  15. https://github.com/aaltodsg/instans-cep2sparql.

References

  1. Abdullah H, Rinne M, Törmä S, Nuutila E (2012) Efficient matching of SPARQL subscriptions using Rete. In: Proceedings of the 27th symposium on applied computing, Riva del Garda, Italy

  2. Anicic D, Fodor P, Rudolph S, Stojanovic N (2011) EP-SPARQL: a unified language for event processing and stream reasoning. In: Proceedings of the 20th international conference on World wide web (WWW’11). ACM, pp 635–644. doi:10.1145/1963405.1963495

  3. Arasu A, Babu S, Widom J (2006) The CQL continuous query language: semantic foundations and query execution. VLDB J 15:121–142. doi:10.1007/s00778-004-0147-z

    Article  Google Scholar 

  4. Babcock B, Babu S, Datar M, Motwani R, Widom J (2002) Models and issues in data stream systems. In: Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on principles of database systems—PODS ’02. ACM Press, New York, p 1. doi:10.1145/543614.543615

  5. Barbieri DF, Braga D, Ceri S, Grossniklaus M (2010a) An execution environment for C-SPARQL queries. In: Proceedings of the 13th international conference on extending database technology-EDBT ’10, Lausanne, Switzerland, p 441. doi:10.1145/1739041.1739095

  6. Barbieri DF, Braga D, Ceri S, Valle ED, Grossniklaus M (2010b) C-SPARQL: a continuous query language for RDF data streams. Int J Semant Comput 04:3. doi:10.1142/S1793351X10000936

    Article  MATH  Google Scholar 

  7. Barbieri DF, Braga D, Ceri S, Valle ED, Grossniklaus M (2010c) Querying RDF streams with C-SPARQL. ACM SIGMOD Rec 39:20. doi:10.1145/1860702.1860705

    Article  MATH  Google Scholar 

  8. Beckett D, Berners-Lee T, Prud’hommeaux E, Carothers G (2014) RDF 1.1 Turtle–Terse RDF triple language. W3C recommendation 25 Feb 2014. http://www.w3.org/TR/turtle/

  9. Bizer C, Cyganiak R (2014) RDF 1.1 TriG W3C recommendation 25 February 2014. http://www.w3.org/TR/trig/

  10. Calbimonte JP, Corcho O, Gray AJG (2010) Enabling ontology-based access to streaming data sources. In: 9th international semantic web conference (ISWC 2010), Shanghai, China, pp 96–111. doi:10.1007/978-3-642-17746-0_7

  11. Cugola G, Margara A (2012) Processing Flows of information: from data stream to complex event processing. ACM Comput Surv 44(i):15:1–15:62. doi:10.1145/2187671.2187677

  12. Dell’Aglio D, Calbimonte JP, Balduini M, Corcho O, Della Valle E (2013) On correctness in RDF stream processor benchmarking. In: International semantic web conference 2013, vol 8219. LNCS, pp 326–342. doi:10.1007/978-3-642-41338-4_21

  13. Depena RK (2010) Diamond: A Rete-match linked data SPARQL environment (M.Sc. Thesis). PhD thesis, University of Texas at Austin

  14. Etzion O, Niblett P, Luckham D (2010) Event processing in action. Manning Publications, Greenwich

    Google Scholar 

  15. Forgy CL (1979) On the efficient implementation of production systems. PhD thesis, Carnegie Mellon University, Pittsburgh, PA, USA, aAI7919143

  16. Forgy CL (1982) Rete: a fast algorithm for the many pattern/many object pattern match problem. Artif Intell 19(1):17–37. doi:10.1016/0004-3702(82)90020-0

    Article  Google Scholar 

  17. Groppe S, Groppe J, Kukulenz D, Linnemann V (2007) A SPARQL engine for streaming RDF data. In: Third international IEEE conference on signal-image technologies and internet-based system. IEEE, pp 167–174. doi:10.1109/SITIS.2007.22

  18. Keskisärkkä R, Blomqvist E (2013) Event object boundaries in RDF streams—a position paper. In: OrdRing 2013—2nd international workshop on ordering and reasoning, CEUR workshop proceedings

  19. Kolchin M, Wetz P, Kiesling E, Tjoa AM (2016) YABench: A comprehensive framework for RDF stream processor correctness and performance assessment. In: Bozzon A, Cudré-Mauroux P, Pautasso C (eds) 16th international conference on web engineering, vol 9671. Springer International Publishing, Lugano, pp 280–298. doi:10.1007/978-3-319-38791-8_16

  20. Komazec S, Cerri D (2011) Towards efficient schema-enhanced pattern matching over RDF data streams. In: 10th ISWC, Springer, Bonn, Germany

  21. Le-Phuoc D, Dao-Tran M, Parreira JX, Hauswirth M (2011) A native and adaptive approach for unified processing of linked streams and linked data. In: ISWC’11. Springer, Berlin, pp 370–388

  22. Luckham D (2002) The power of events: an introduction to complex event processing in distributed enterprise systems, 1st edn. Addison-Wesley Professional, Boston

    Google Scholar 

  23. Luckham D, Schulte R (2011) Event processing glossary D version 2.0. http://www.complexevents.com/

  24. Miranker DP, Depena RK, Hyunjoon J, Carlos R, Sequeda JF (2012) Diamond: a SPARQL query engine, for linked data based on the rete match. In: Gueret C, Sharffle F, Ienco D, Villata S (eds) 1st international workshop on artificial intelligence meets the web of data (ECAI 2012), Montpellier, pp 12–17

  25. Rinne M, Nuutila E (2014) Constructing event processing systems of layered and heterogeneous events with SPARQL. In: Meersman R, Panetto H, Dillon T, Missikoff M, Liu L, Pastor O, Cuzzocrea A, Sellis T (eds) On the move to meaningful internet systems: OTM 2014 conferences. Springer Berlin, pp 682–699. doi:10.1007/978-3-662-45563-0_42

  26. Rinne M, Abdullah H, Törmä S, Nuutila E (2012) Processing Heterogeneous RDF Events with Standing SPARQL Update Rules. In: Meersman R, Dillon T (eds) OTM 2012 conferences, part II. Springer, pp 793–802

  27. Rinne M, Törmä S, Nuutila E (2012b) SPARQL-based applications for RDF-encoded sensor data. In: 5th international workshop on semantic sensor networks

  28. Rinne M, Blomqvist E, Keskisärkkä R, Nuutila E (2013) Event processing in RDF. In: Proceedings of WOP2013, CEUR workshop proceedings, p 13

  29. Rinne M, Solanki M, Nuutila E (2016) RFID-based logistics monitoring with semantics-driven event processing. Proceedings of the 10th ACM international conference on distributed and event-based systems, pp 238–245. doi:10.1145/2933267.2933300

  30. Taylor K, Leidinger L (2011) Ontology-driven complex event processing in heterogeneous sensor networks. In: 8th extended semantic web conference (ESWC). Springer, Berlin, pp 285–299. doi:10.1007/978-3-642-21064-8_20

  31. Terry D, Goldberg D, Nichols D, Oki B (1992) Continuous queries over append-only databases. ACM SIGMOD Rec 21(2):321–330. doi:10.1145/141484.130333

    Article  Google Scholar 

  32. Teymourian K, Rohde M, Paschke A (2012) Fusion of background knowledge and streams of events. In: Proceedings of the 6th ACM international conference on distributed event-based systems—DEBS ’12. ACM Press, New York, pp 302–313. doi:10.1145/2335484.2335517

  33. Tommasini R, Valle ED, Balduini M, Aglio DD (2015) Heaven Test Stand: towards comparative research on RSP engines. In: Joint Proceedings of the 1st Joint International Workshop on semantic sensor networks and terra cognita (SSN-TC 2015) and the 4th international workshop on ordering and reasoning (OrdRing 2015), CEUR workshop proceedings, Bethlehem, PA, USA, pp 1–16

  34. W3C (2013a) SPARQL 1.1 query language W3C recommendation 21.3.2013. http://www.w3.org/TR/sparql11-query/

  35. W3C (2013b) SPARQL 1.1 update W3C recommendation 21 March 2013. http://www.w3.org/TR/sparql11-update/

  36. W3C (2014) RDF 1.1 Concepts and abstract syntax W3C recommendation 25 February 2014. http://www.w3.org/TR/rdf11-concepts/

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Acknowledgments

This work has been carried out in Spaceify, SPIRE and TrafficSense projects funded by European Commission through the SSRA (Smart Space Research and Applications) activity of EIT ICT Labs (http://eit.ictlabs.eu/ict-labs/thematic-action-lines/smart-spaces/), Tekes and Aalto University.

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  1. Aalto University School of Science, PO Box 15400, 00076, Aalto, Finland

    Mikko Rinne & Esko Nuutila

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  1. Mikko Rinne
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Correspondence to Mikko Rinne.

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Rinne, M., Nuutila, E. Constructing Event Processing Systems of Layered and Heterogeneous Events with SPARQL. J Data Semant 6, 57–69 (2017). https://doi.org/10.1007/s13740-016-0073-4

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  • DOI: https://doi.org/10.1007/s13740-016-0073-4

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