A First Step Towards Stream Reasoning

  • Emanuele Della Valle
  • Stefano Ceri
  • Davide Francesco Barbieri
  • Daniele Braga
  • Alessandro Campi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5468)

Abstract

While reasoners are year after year scaling up in the classical, time invariant domain of ontological knowledge, reasoning upon rapidly changing information has been neglected or forgotten. On the contrary, processing of data streams has been largely investigated and specialized Stream Database Management Systems exist. In this paper, by coupling reasoners with powerful, reactive, throughput-efficient stream management systems, we introduce the concept of Stream Reasoning. We expect future realization of such concept to have high impact on the future Internet because it enables reasoning in real time, at a throughput and with a reactivity not obtained in previous works.

Keywords

Data Streams Reasoning Real-time Throughput-efficiency Urban Computing Pervasive Computing 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Kiryakov, A.: Measurable targets for scalable reasoning (2007)Google Scholar
  2. 2.
    Garofalakis, M., Gehrke, J., Rastogi, R.: Data Stream Management: Processing High-Speed Data Streams (Data-Centric Systems and Applications). Springer, New York (2007)Google Scholar
  3. 3.
    Kindberg, T., Chalmers, M., Paulos, E.: Guest editors’ introduction: Urban computing. IEEE Pervasive Computing 6(3), 18–20 (2007)CrossRefGoogle Scholar
  4. 4.
    Arikawa, M., Konomi, S., Ohnishi, K.: Navitime: Supporting pedestrian navigation in the real world. IEEE Pervasive Computing 6(3), 21–29 (2007)CrossRefGoogle Scholar
  5. 5.
    Reades, J., Calabrese, F., Sevtsuk, A., Ratti, C.: Cellular census: Explorations in urban data collection. IEEE Pervasive Computing 6(3), 30–38 (2007)CrossRefGoogle Scholar
  6. 6.
    Bassoli, A., Brewer, J., Martin, K., Dourish, P., Mainwaring, S.: Underground aesthetics: Rethinking urban computing. IEEE Pervasive Computing 6(3), 39–45 (2007)CrossRefGoogle Scholar
  7. 7.
    Fensel, D., van Harmelen, F., Andersson, B., Brennan, P., Cunningham, H., Della Valle, E., Fischer, F., Huang, Z., Kiryakov, A., il Lee, T.K., School, L., Tresp, V., Wesner, S., Witbrock, M., Zhong, N.: Towards larkc: a platform for web-scale reasoning. In: IEEE International Conference on Semantic Computing, ICSC 2008 (August 2008)Google Scholar
  8. 8.
    Fensel, D., van Harmelen, F.: Unifying reasoning and search to web scale. IEEE Internet Computing 11(2), 9695 (2007)CrossRefGoogle Scholar
  9. 9.
    Shvaiko, P., Giunchiglia, F., Bundy, A., Besana, P., Sierra, C., Van Harmelen, F., Zaihrayeu, I.: Benchmarking methodology for good enough answers. Technical report, DISI-08-003, Informatica e Telecomunicazioni, University of Trento (2008)Google Scholar
  10. 10.
    Tatbul, N., Çetintemel, U., Zdonik, S., Cherniack, M., Stonebraker, M.: Load shedding in a data stream manager. In: VLDB 2003: Proceedings of the 29th international conference on Very large data bases, VLDB Endowment, pp. 309–320 (2003)Google Scholar
  11. 11.
    Tatbul, N., Cetintemel, U., Zdonik, S., Cherniak, M., Stonebraker, M.: Exploiting punctuation semantics in continuous data streams. IEEE Trans. on Knowledge and Data Eng. 15(3), 555–568 (2003)CrossRefGoogle Scholar
  12. 12.
    Thaper, N., Guha, S., Indyk, P., Koudas, N.: Dynamic multidimensional histograms. In: SIGMOD 2002: Proceedings of the 2002 ACM SIGMOD international conference on Management of data, pp. 428–439. ACM, New York (2002)Google Scholar
  13. 13.
    Chakrabarti, K., Garofalakis, M., Rastogi, R., Shim, K.: Approximate query processing using wavelets. The VLDB Journal 10(2-3), 199–223 (2001)MATHGoogle Scholar
  14. 14.
    Bloom, B.H.: Space/time trade-offs in hash coding with allowable errors. Commun. ACM 13(7), 422–426 (1970)CrossRefMATHGoogle Scholar
  15. 15.
    Ding, L., Finin, T., Peng, Y., da Silva, P.P., McGuinness, D.L.: Tracking RDF Graph Provenance using RDF Molecules. Technical report, UMBC (April 2005)Google Scholar
  16. 16.
    Volz, R., Staab, S., Motik, B.: Incrementally maintaining materializations of ontologies stored in logic databases. J. Data Semantics 2, 1–34 (2005)MATHGoogle Scholar
  17. 17.
    Mendelzon, A.O., Rizzolo, F., Vaisman, A.: Indexing temporal xml documents. In: VLDB 2004: Proceedings of the Thirtieth international conference on Very large data bases, VLDB Endowment, pp. 216–227 (2004)Google Scholar
  18. 18.
    Babu, S., Widom, J.: Continuous queries over data streams. SIGMOD Rec. 30(3), 109–120 (2001)CrossRefGoogle Scholar
  19. 19.
    Babcock, B., Babu, S., Datar, M., Motwani, R., Widom, J.: Models and issues in data stream systems. In: PODS 2002: Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems, pp. 1–16. ACM, New York (2002)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Emanuele Della Valle
    • 1
  • Stefano Ceri
    • 1
  • Davide Francesco Barbieri
    • 1
  • Daniele Braga
    • 1
  • Alessandro Campi
    • 1
  1. 1.Dip. di Elettronica e InformazionePolitecnico di MilanoMilanoItaly

Personalised recommendations