Advertisement

Processing Ubiquitous Personal Event Streams to Provide User-Controlled Support

  • Jeremy Debattista
  • Simon Scerri
  • Ismael Rivera
  • Siegfried Handschuh
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8181)

Abstract

The increase in use of smart devices nowadays provides us with a lot of personal data and context information. In this paper we describe an approach which allows users to define and register rules based on their personal data activities in an event processor, which continuously listens to perceived context data and triggers any satisfied rules. We describe the Rule Management Ontology (DRMO) as a means to define rules using a standard format, whilst providing a scalable solution in the form of a Rule Network Event Processor which detects and analyses events, triggering rules which are satisfied. Following an evaluation of the network v.s. a simplistic sequential approach, we justify a trade-off between initialisation time and processing time.

Keywords

Resource Description Framework Smart Device Resource Type SPARQL Query Triple Pattern 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Attard, J., Scerri, S., Rivera, I., Handschuh, S.: Ontology-based situation recognition for context-aware systems. In: I-SEMANTICS (2013)Google Scholar
  2. 2.
    Beltran, V., Arabshian, K., Schulzrinne, H.: Ontology-based user-defined rules and context-aware service composition system. In: García-Castro, R., Fensel, D., Antoniou, G. (eds.) ESWC 2011. LNCS, vol. 7117, pp. 139–155. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  3. 3.
    Debattista, J., Scerri, S., Rivera, I., Handschuh, S.: Ontology-based rules for recommender systems. In: Proceedings of the International Workshop on Semantic Technologies meet Recommender Systems & Big Data (2012)Google Scholar
  4. 4.
    Decker, S., Melnik, S., van Harmelen, F., Fensel, D., Klein, M., Broekstra, J., Erdmann, M., Horrocks, I.: The semantic web: the roles of xml and rdf. IEEE Internet Computing 4(5), 63–73 (2000)CrossRefGoogle Scholar
  5. 5.
    Forgy, C.L.: Rete: A fast algorithm for the many pattern/many object pattern match problem. Artificial Intelligence 19(1), 17–37 (1982)CrossRefGoogle Scholar
  6. 6.
    Mendes, M.R.N., Bizarro, P., Marques, P.: A performance study of event processing systems. In: Nambiar, R., Poess, M. (eds.) TPCTC 2009. LNCS, vol. 5895, pp. 221–236. Springer, Heidelberg (2009)Google Scholar
  7. 7.
    Scerri, S., Schuller, A., Rivera, I., Attard, J., Debattista, J., Valla, M., Hermann, F., Handschuh, S.: Interacting with a context-aware personal information sharing system. In: Kurosu, M. (ed.) HCII/HCI 2013, Part V. LNCS, vol. 8008, pp. 122–131. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  8. 8.
    Sintek, M., Handschuh, S., Scerri, S., van Elst, L.: Technologies for the social semantic desktop. In: Tessaris, S., Franconi, E., Eiter, T., Gutierrez, C., Handschuh, S., Rousset, M.-C., Schmidt, R.A. (eds.) Reasoning Web. LNCS, vol. 5689, pp. 222–254. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  9. 9.
    Walzer, K., Breddin, T., Groch, M.: Relative temporal constraints in the rete algorithm for complex event detection. In: DEBS, pp. 147–155 (2008)Google Scholar
  10. 10.
    Yen, J.Y.: Finding the K Shortest Loopless Paths in a Network. Management Science (1971)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Jeremy Debattista
    • 1
  • Simon Scerri
    • 1
  • Ismael Rivera
    • 1
  • Siegfried Handschuh
    • 1
  1. 1.Digital Enterprise Research InstituteNational University of IrelandGalwayIreland

Personalised recommendations