Enabling Ontology-Based Access to Streaming Data Sources

  • Jean-Paul Calbimonte
  • Oscar Corcho
  • Alasdair J. G. Gray
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6496)


The availability of streaming data sources is progressively increasing thanks to the development of ubiquitous data capturing technologies such as sensor networks. The heterogeneity of these sources introduces the requirement of providing data access in a unified and coherent manner, whilst allowing the user to express their needs at an ontological level. In this paper we describe an ontology-based streaming data access service. Sources link their data content to ontologies through s 2o mappings. Users can query the ontology using sparql Stream, an extension of sparql for streaming data. A preliminary implementation of the approach is also presented. With this proposal we expect to set the basis for future efforts in ontology-based streaming data integration.


Sensor Network Query Processing Streaming Data Conjunctive Query Continuous Query 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Barrasa, J., Corcho, O., Gómez-Pérez, A.: R2O, an extensible and semantically based database-to-ontology mapping language. In: SWDB 2004, pp. 1069–1070 (2004)Google Scholar
  2. 2.
    Bizer, C., Cyganiak, R.: D2RQ. Lessons Learned. In: W3C Workshop on RDF Access to Relational Databases (October 2007)Google Scholar
  3. 3.
    Sahoo, S.S., Halb, W., Hellmann, S., Idehen, K., Thibodeau Jr, T., Auer, S., Sequeda, J., Ezzat, A.: A survey of current approaches for mapping of relational databases to RDF. W3C (January 2009)Google Scholar
  4. 4.
    Arasu, A., Babcock, B., Babu, S., Cieslewicz, J., Datar, M., Ito, K., Motwani, R., Srivastava, U., Widom, J.: Stream: The stanford data stream management system. In: Garofalakis, M., Gehrke, J., Rastogi, R. (eds.) Data Stream Management (2006)Google Scholar
  5. 5.
    Abadi, D.J., Ahmad, Y., Balazinska, M., Cetintemel, U., Cherniack, M., Hwang, J.H., Lindner, W., Maskey, A.S., Rasin, A., Ryvkina, E., Tatbul, N., Xing, Y., Zdonik, S.: The Design of the Borealis Stream Processing Engine. In: CIDR 2005 (2005)Google Scholar
  6. 6.
    Galpin, I., Brenninkmeijer, C.Y., Jabeen, F., Fernandes, A.A., Paton, N.W.: Comprehensive optimization of declarative sensor network queries. In: SSDBM 2009, pp. 339–360 (2009)Google Scholar
  7. 7.
    Madden, S.R., Franklin, M.J., Hellerstein, J.M., Hong, W.: TinyDB: an acquisitional query processing system for sensor networks. ACM Trans. Database Syst. 30(1), 122–173 (2005)CrossRefGoogle Scholar
  8. 8.
    Bolles, A., Grawunder, M., Jacobi, J.: Streaming SPARQL - extending SPARQL to process data streams. In: Bechhofer, S., Hauswirth, M., Hoffmann, J., Koubarakis, M. (eds.) ESWC 2008. LNCS, vol. 5021, pp. 448–462. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  9. 9.
    Barbieri, D.F., Braga, D., Ceri, S., Grossniklaus, M.: An execution environment for C-SPARQL queries. In: EDBT 2010, Lausanne, Switzerland, pp. 441–452 (March 2010)Google Scholar
  10. 10.
    Lenzerini, M.: Data integration: a theoretical perspective. In: PODS 2002, pp. 233–246 (2002)Google Scholar
  11. 11.
    Golab, L., Özsu, M.T.: Issues in data stream management. SIGMOD Record 32(2), 5–14 (2003)CrossRefGoogle Scholar
  12. 12.
    Brenninkmeijer, C.Y., Galpin, I., Fernandes, A.A., Paton, N.W.: A semantics for a query language over sensors, streams and relations. In: Gray, A., Jeffery, K., Shao, J. (eds.) BNCOD 2008. LNCS, vol. 5071, pp. 87–99. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  13. 13.
    Arasu, A., Babu, S., Widom, J.: The CQL continuous query language: semantic foundations and query execution. The VLDB Journal 15(2), 121–142 (2006)CrossRefGoogle Scholar
  14. 14.
    Kossmann, D.: The state of the art in distributed query processing. ACM Comput. Surv. 32(4), 422–469 (2000)CrossRefGoogle Scholar
  15. 15.
    Pérez, J., Arenas, M., Gutierrez, C.: Semantics and complexity of SPARQL. ACM Trans. Database Syst. 34(3), 1–45 (2009)CrossRefGoogle Scholar
  16. 16.
    Calvanese, D., De Giacomo, G., Lembo, D., Lenzerini, M., Rosati, R.: DL-Lite: Tractable description logics for ontologies. In: AAAI 2005, pp. 602–607 (2005)Google Scholar
  17. 17.
    Poggi, A., Lembo, D., Calvanese, D., Giacomo, G.D., Lenzerini, M., Rosati, R.: Linking data to ontologies. J. Data Semantics 10, 133–173 (2008)zbMATHGoogle Scholar
  18. 18.
    Lubyte, L., Tessaris, S.: Supporting the development of data wrapping ontologies. In: 4th Asian Semantic Web Conference (December 2009)Google Scholar
  19. 19.
    Erling, O., Mikhailov, I.: RDF support in the Virtuoso DBMS. In: Conference on Social Semantic Web. LNI, vol. 113, pp. 59–68. GI (2007)Google Scholar
  20. 20.
    Chandrasekaran, S., Cooper, O., Deshpande, A., Franklin, M.J., Hellerstein, J.M., Hong, W., Krishnamurthy, S., Madden, S.R., Reiss, F., Shah, M.A.: TelegraphCQ: continuous dataflow processing. In: SIGMOD 2003, p. 668 (2003)Google Scholar
  21. 21.
    Yao, Y., Gehrke, J.: The Cougar approach to in-network query processing in sensor networks. SIGMOD Rec. 31(3), 9–18 (2002)CrossRefGoogle Scholar
  22. 22.
    Harris, S., Seaborne, A. (eds.): SPARQL 1.1 query language. Working draft, W3C (2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Jean-Paul Calbimonte
    • 1
  • Oscar Corcho
    • 1
  • Alasdair J. G. Gray
    • 2
  1. 1.Ontology Engineering Group, Departamento de Inteligencia Artificial, Facultad de InformáticaUniversidad Politécnica de MadridBoadilla del MonteSpain
  2. 2.School of Computer ScienceThe University of ManchesterManchesterUnited Kingdom

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