A Stream-Temporal Query Language for Ontology Based Data Access

  • Özgür Lütfü Özçep
  • Ralf Möller
  • Christian Neuenstadt
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8736)


The paper contributes to the recent efforts on temporalizing and streamifiying ontology based data access (OBDA) by discussing aspects of rewritability, i.e., compilability of the TBox into ontology-level queries, and unfoldability, i.e., transformability of ontology-level queries to queries on datasource level, for the new query-language framework STARQL. The distinguishing feature of STARQL is its general stream windowing and ABox sequencing strategy which allows it to plugin well-known query languages such as unions of conjunctive queries (UCQs) in combination with TBox languages such as DL-Lite and do temporal reasoning with a sorted first-order logic on top of them. The paper discusses safety aspects under which STARQL queries that embed UCQs over DL-Lite ontologies can be rewritten and unfolded to back-end relational stream query languages such as CQL. With these results, the adoption of description logic technology in industrially relevant application areas such as industrial monitoring is crucially fostered.


streams OBDA monitoring unfolding safety 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Abiteboul, S., Hull, R., Vianu, V.: Foundations of Databases. Addison-Wesley (1995)Google Scholar
  2. 2.
    Anicic, D., Rudolph, S., Fodor, P., Stojanovic, N.: Stream reasoning and complex event processing in ETALIS. Semantic Web 3(4), 397–407 (2012)Google Scholar
  3. 3.
    Arasu, A., Babu, S., Widom, J.: The CQL continuous query language: semantic foundations and query execution. The VLDB Journal 15, 121–142 (2006)CrossRefGoogle Scholar
  4. 4.
    Artale, A., Kontchakov, R., Wolter, F., Zakharyaschev, M.: Temporal description logic for ontology-based data access. In: Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence, IJCAI 2013, pp. 711–717 (2013)Google Scholar
  5. 5.
    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)CrossRefGoogle Scholar
  6. 6.
    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)CrossRefGoogle Scholar
  7. 7.
    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)CrossRefGoogle Scholar
  8. 8.
    Chandrasekaran, S., Cooper, O., Deshpande, A., Franklin, M.J., Hellerstein, J.M., Hong, W., Krishnamurthy, S., Madden, S., Raman, V., Reiss, F., Shah, M.A.: TelegraphCQ: Continuous dataflow processing for an uncertain world. In: CIDR (2003)Google Scholar
  9. 9.
    Chomicki, J., Toman, D.: Temporal databases. In: Handbook of Temporal Reasoning in Artificial Intelligence, vol. 1, pp. 429–467. Elsevier (2005)Google Scholar
  10. 10.
    Cugola, G., Margara, A.: TESLA: A formally defined event specification language. In: Proceedings of the Fourth ACM International Conference on Distributed Event-Based Systems, DEBS 2010, pp. 50–61. ACM, New York (2010)Google Scholar
  11. 11.
    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)CrossRefGoogle Scholar
  12. 12.
    Hwang, J.H., Xing, Y., Çetintemel, U., Zdonik, S.B.: A cooperative, self-configuring high-availability solution for stream processing. In: ICDE, pp. 176–185 (2007)Google Scholar
  13. 13.
    Jain, N., Mishra, S., Srinivasan, A., Gehrke, J., Widom, J., Balakrishnan, H., Çetintemel, U., Cherniack, M., Tibbetts, R., Zdonik, S.: Towards a streaming SQL standard. Proc. VLDB Endow. 1(2), 1379–1390 (2008)CrossRefGoogle Scholar
  14. 14.
    Krämer, J., Seeger, B.: Semantics and implementation of continuous sliding window queries over data streams. ACM Trans. Database Syst. 34(1), 1–49 (2009)CrossRefGoogle Scholar
  15. 15.
    Özçep, O.L., Möller, R., Neuenstadt, C.: Obda stream access combined with safe first-order temporal reasoning. Techn. report, Hamburg Univ. of Technology (2014)Google Scholar
  16. 16.
    Özçep, Ö.L., Möller, R., Neuenstadt, C., Zheleznyakov, D., Kharlamov, E.: Deliverable D5.1 – a semantics for temporal and stream-based query answering in an OBDA context. Deliverable FP7-318338, EU (October 2013)Google Scholar
  17. 17.
    Le-Phuoc, D., Dao-Tran, M., Parreira, J.X., 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)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Özgür Lütfü Özçep
    • 1
  • Ralf Möller
    • 1
  • Christian Neuenstadt
    • 1
  1. 1.Institute for Softwaresystems (STS)Hamburg University of TechnologyHamburgGermany

Personalised recommendations