Advertisement

Semantic Stream Processing in Dynamic Environments Using Dynamic Stream Selection

  • Michael JacobyEmail author
  • Till Riedel
Conference paper
Part of the Technologien für die intelligente Automation book series (TIA)

Abstract

Cyber-physical systems (CPS) require a new level of dynamics in information processing. Databases and query approaches need to be extended towards dynamic stream aggregation and analysis systems. In this paper, we designed ECQELS, a semantic stream processing engine, to support CPS applications by adding essential features like dynamic sensor selection. We present a feature complete first implementation and show competitive performance results.

Keywords

stream processing semantic streaming dynamic stream selection ECQELS 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Arasu, A., Babu, S., Widom, J. (2006). The CQL continuous query language: Semantic foundations and query execution. VLDB Journal, 15(2), 121-142. doi:10.1007/s00778-004-0147-zGoogle Scholar
  2. 2.
    Prud’Hommeaux, Eric, and Andy Seaborne. SPARQL query language for RDF. W3C recommendation 15 (2008).Google Scholar
  3. 3.
    Jacoby, Michael. (2011). Enabling Domain-Specific Rule-Based Automation With Semantic Stream Technology (Master’s Thesis). doi: 10.5445/IR/1000056564
  4. 4.
    Danh, L.-P., Minh, D.-T., Parreira, J. X., Hauswirth, M. (2011). A Native and Adaptive Approach for Unified Processing of Linked Streams and Linked Data. Semantic Web - Iswc 2011, Pt I, 7031(24761), 370-388.Google Scholar
  5. 5.
    Cugola, G., & Margara, A. (2011). Processing Flows of Information : From Data Stream to Complex Event Processing, V(i), 359-360.Google Scholar
  6. 6.
    Arasu, A., Babu, S., & Widom, J. (2006). The CQL continuous query language: Semantic foundations and query execution. VLDB Journal, 15(2), 121-142. doi: 10.1007/s00778-004-0147-z
  7. 7.
    Barbieri, D. F. (2009). C-SPARQL : SPARQL for Continuous Querying. Language, 427(c), 1061-1062. doi: 10.1145/1526709.1526856
  8. 8.
    Calbimonte, J.-P., Oscar, C., & Gray, A. (2010). Ontology-based Access to Streaming Data Sources. 7th Extended Semantic Web Conference ESWC2010, 6496 LNCS(PART 1), 2-3.Google Scholar
  9. 9.
    Mileo, A., Abdelrahman, A., Policarpio, S., & Hauswirth, M. (2013). StreamRule: A nonmonotonic stream reasoning system for the semantic web. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7994 LNCS, 247-252.Google Scholar
  10. 10.
    Rinne, M., Nuutila, E., & Törmä, S. (2012). INSTANS: High-performance event processing with standard RDF and SPARQL. CEURWorkshop Proceedings, 914, 101-104. doi: 10.1109/ICDE.2013.6544856
  11. 11.
    Anicic, D., & Fodor, P. (2011). EP-SPARQL: a unified language for event processing and stream reasoning. Proceedings of the 20th international conference on World wide web, 635-644. doi: 10.1147/sj.433.0598
  12. 12.
    Zhou, Q., Simmhan, Y., & Prasanna, V. (2012). SCEPter : Semantic Complex Event Processing over End-to-End Data Flows, (April), 1-20.Google Scholar
  13. 13.
    Dao-Tran, M., & Le-Phuoc, D. (2015). Towards enriching CQELS with Complex Event Processing and path navigation. CEUR Workshop Proceedings, 1447, 2-14.Google Scholar
  14. 14.
    Aglio, D. D., Calbimonte, J., Valle, E. Della, & Corcho, O. (n.d.). Towards A Unified Language for RDF Stream Query Processing.Google Scholar

Copyright information

© Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  1. 1.KarlsruheGermany

Personalised recommendations