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Stream-Query Compilation with Ontologies

Part of the Lecture Notes in Computer Science book series (LNAI,volume 9457)


Rational agents perceiving data from a dynamic environment and acting in it have to be equipped with capabilities such as decision making, planning etc. We assume that these capabilities are based on query answering with respect to (high-level) streams of symbolic descriptions, which are grounded in (low-level) data streams. Queries need to be answered w.r.t. an ontology. The central idea is to compile ontology-based stream queries (continuous or historical) to relational data processing technology, for which efficient implementations are available. We motivate our query language STARQL (Streaming and Temporal ontology Access with a Reasoning-Based Query Language) with a sensor data processing scenario, and compare the approach realized in the STARQL framework with related approaches regarding expressivity.


  • Stream
  • Rewriting
  • Ontology
  • Description logic

This work has been supported by the European Commission as part of the FP7 project Optique.

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  1. Arasu, A., Babu, S., Widom, J.: The CQL continuous query language: semantic foundations and query execution. VLDB J. 15, 121–142 (2006)

    CrossRef  Google Scholar 

  2. Artale, A., Kontchakov, R., Wolter, F., Zakharyaschev, M.: Temporal description logic for ontology-based data access. In: IJCAI 2013, pp. 711–717 (2013)

    Google Scholar 

  3. Avron, A.: Constructibility and decidability versus domain independence and absoluteness. Theor. Comput. Sci. 394(3), 144–158 (2008)

    MathSciNet  CrossRef  MATH  Google Scholar 

  4. Baader, F., Bauer, A., Baumgartner, P., Cregan, A., Gabaldon, A., Ji, K., Lee, K., Rajaratnam, D., Schwitter, R.: A novel architecture for situation awareness systems. In: Giese, M., Waaler, A. (eds.) TABLEAUX 2009. LNCS, vol. 5607, pp. 77–92. Springer, Heidelberg (2009)

    CrossRef  Google Scholar 

  5. Baader, F., Borgwardt, S., Lippmann, M.: Temporalizing ontology-based data access. In: Bonacina, M.P. (ed.) CADE 2013. LNCS, vol. 7898, pp. 330–344. Springer, Heidelberg (2013)

    CrossRef  Google Scholar 

  6. Borgwardt, S., Lippmann, M., Thost, V.: Temporal query answering in the description logic DL-lite. In: Fontaine, P., Ringeissen, C., Schmidt, R.A. (eds.) FroCoS 2013. LNCS, vol. 8152, pp. 165–180. Springer, Heidelberg (2013)

    CrossRef  Google Scholar 

  7. Calbimonte, J.P., Jeung, H., Corcho, O., Aberer, K.: Enabling query technologies for the semantic sensor web. Int. J. Semant. Web Inf. Syst. 8(1), 43–63 (2012)

    CrossRef  Google Scholar 

  8. Calvanese, D., De Giacomo, G., Lembo, D., Lenzerini, M., Poggi, A., Rodriguez-Muro, M., Rosati, R.: Ontologies and databases: the DL-lite approach. In: Tessaris, S., Franconi, E., Eiter, T., Gutierrez, C., Handschuh, S., Rousset, M.-C., Schmidt, R.A. (eds.) Reasoning Web. LNCS, vol. 5689, pp. 255–356. Springer, Heidelberg (2009)

    Google Scholar 

  9. Della Valle, E., Ceri, S., Barbieri, D.F., Braga, D., Campi, A.: A first step towards stream reasoning. In: Domingue, J., Fensel, D., Traverso, P. (eds.) FIS 2008. LNCS, vol. 5468, pp. 72–81. Springer, Heidelberg (2009)

    CrossRef  Google Scholar 

  10. Heintz, F., Kvarnström, J., Doherty, P.: Bridging the sense-reasoning gap: Dyknow - stream-based middleware for knowledge processing. Adv. Eng. Inform. 24(1), 14–26 (2010)

    CrossRef  Google Scholar 

  11. Özçep, Ö.L., Möller, R., Neuenstadt, C.: A stream-temporal query language for ontology based data access. In: Lutz, C., Thielscher, M. (eds.) KI 2014. LNCS, vol. 8736, pp. 183–194. Springer, Heidelberg (2014)

    Google Scholar 

  12. Le-Phuoc, D., Dao-Tran, M., Xavier Parreira, J., Hauswirth, M.: A Native and adaptive approach for unified processing of linked streams and linked data. In: Aroyo, L., Welty, C., Alani, H., Taylor, J., Bernstein, A., Kagal, L., Noy, N., Blomqvist, E. (eds.) ISWC 2011, Part I. LNCS, vol. 7031, pp. 370–388. Springer, Heidelberg (2011)

    CrossRef  Google Scholar 

  13. Russell, S.J., Norvig, P.: Artificial Intelligence - A Modern Approach. Prentice Hall, Egnlewood Cliffs (1995)

    MATH  Google Scholar 

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Correspondence to Özgür Lütfü Özçep .

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Özçep, Ö.L., Möller, R., Neuenstadt, C. (2015). Stream-Query Compilation with Ontologies. In: Pfahringer, B., Renz, J. (eds) AI 2015: Advances in Artificial Intelligence. AI 2015. Lecture Notes in Computer Science(), vol 9457. Springer, Cham.

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