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Information Systems Frontiers

, Volume 14, Issue 2, pp 317–330 | Cite as

Modeling trade-offs in the design of sensor-based event processing infrastructures

  • Agnès Voisard
  • Holger ZiekowEmail author
Article

Abstract

Systems for distributed event processing have recently gained increasing attention in a broad range of application domains. This raises the demand for methods to adapt the system design to application-specific needs. Our approach considers (1) trade-offs regarding the hardware infrastructure and (2) trade-offs in the software design. For the underlying model we categorize events along the dimensions of temporal complexity and physical distribution. This yields a categorization of events that drives trade-offs in the infrastructure design. The presented model supports design decisions in dependence on application-specific event properties and design goals.

Keywords

Event-based systems Distributed event processing Architecture Design guidelines 

Notes

Acknowledgements

We wish to thank Katharina Hahn and Kirsten Terfloth for useful feedback. Part of this work beneficiated from collaboration with them. A proportion of work on this paper was carried out when Agnès Voisard was visiting the International Computer Science Institute in Berkeley, USA.

References

  1. Ahmad, Y., & Çetintemel, U. (2004). Network-aware query processing for stream-based applications. In VLDB ’04: Proceedings of the thirtieth international conference on very large data bases (pp. 456–467). VLDB Endowment.Google Scholar
  2. Arasu, A., Babu, S., & Widom, J. (2006). The cql continuous query language: Semantic foundations and query execution. The VLDB Journal, 15(2), 121–142.CrossRefGoogle Scholar
  3. Bornhövd, C., Lin, T., Haller, S., & Schaper, J. (2004). Integrating automatic data acquisition with business processes experiences with sap’s auto-id infrastructure. In VLDB ’04: Proceedings of the thirtieth international conference on very large data bases (pp. 1182–1188). VLDB Endowment.Google Scholar
  4. Carlson, J., & Lisper, B. (2004). An event detection algebra for reactive systems. In Proc. of the 4th ACM intl. conf. on embedded software (EMSOFT). New York, NY, USA: ACM.Google Scholar
  5. Chakravarthy, S., & Mishra, D. (1994). SNOOP: An expressive event specification language for active databases. Journal of Data and Knowledge Engineering, 14(1).Google Scholar
  6. Decker, C., Kubach, U., & Beigl, M. (2003). Revealing the retail black box by interaction sensing. In ICDCSW ’03: Proceedings of the 23rd international conference on distributed computing systems (p. 328). Washington, DC, USA: IEEE Computer Society.Google Scholar
  7. Doolin, D., & Sitar, N. (2005). Wireless sensors for wildfire monitoring. In Proceedings of the SPIE symposium on smart structures & materials/ NDE 2005.Google Scholar
  8. Franklin, M. J., Jeffery, S. R., Krishnamurthy, S., & Reiss, F. (2005). Design considerations for high fan-in systems: The hifi approach. In Proc. of the int. conf. on innovative data systems research (CIDR).Google Scholar
  9. Gatziu, S., & Dittrich, K. R. (1992). SAMOS: An active object-oriented database system. IEEE Quarterly Bulletin on Data Engineering, Special Issue on Active Databases, 15(1–4), 23–26.Google Scholar
  10. Guerrero, P. E., Sachs, K., Cilia, M., Bornhövd, C., & Buchmann, A. (2007). Pushing business data processing towards the periphery. In 23rd international conference on data engineering (ICDE’07) (pp. 1485–1486). New York, NY: IEEE Computer Society.CrossRefGoogle Scholar
  11. GTZ-Potsdam (2007). German indonesian tsunami early warning system. http://www.gitews.org/.
  12. Ivanytsynova, L., Ziekow, H., Günther, O., Kletti, W., & Kubach, U. (2008). Six case studies. In O. Günther, W. Kletti, U. Kubach (Eds.), RFID in manufacturing. Berlin, Heidelberg: Springer.Google Scholar
  13. Kounev, S., Sachs, K., Bacon, J., & Buchmann, A. (2008). A methodology for performance modeling of distributed event-based systems. In ISORC ’08: Proceedings of the 2008 11th IEEE symposium on object oriented real-time distributed computing (pp. 13–22). New York, N. Y.: IEEE Computer Society. doi: 10.1109/ISORC.2008.51.
  14. Madden, S., Franklin, M. J., Hellerstein, J. M., & Hong, W. (2002). Tag: A tiny aggregation service for ad-hoc sensor networks. SIGOPS Operating Systems Review, 36(SI), 131–146.CrossRefGoogle Scholar
  15. Madden, S. R., Franklin, M. J., Hellerstein, J. M., & Hong, W. (2003). The design of an acquisitional query processor for sensor networks. In Proceedings of the ACM SIGMOD.Google Scholar
  16. Muehl, G., Fiege, L., & Pietzuch, P. R. (2006). Distributed event-based systems. New York: Springer.Google Scholar
  17. Pietzuch, P. R., Ledlie, J., Shneidman, J., Roussopoulos, M., Welsh, M., & Seltzer, M. I. (2006). Network-aware operator placement for stream-processing systems. In ICDE (p. 49).Google Scholar
  18. Ratnasamy, S., Karp, B., Shenker, S., Estrin, D., Govindan, R., Yin, L., et al. (2003). Data-centric storage in sensornets with GHT, a geographic hash table. Mobile Networks and Applications (MONET), 8(4), 427–442. (Special Issue on Wireless Sensor Networks).CrossRefGoogle Scholar
  19. Srivastava, U., Munagala, K., & Widom, J. (2005). Operator placement for in-network stream query processing. In Proceedings of ACM SIGMOD-SIGACT-SIGART symposium on principles of database systems (PODS). New York, NY, USA: ACM.Google Scholar
  20. Terfloth, K., Hahn, K., & Voisard, A. (2007). On the cost of shifting event processing within wireless environments. In Proceedings of the dagstuhl event processing workshop.Google Scholar
  21. Wieland, M., Griesser, L., & Kuendig, C. (2000). Seismic early warning system for a nuclear power plant. In Proceedings of the 12th world conference on earthquake engineering (WCEE 2000).Google Scholar
  22. Yao, Y., & Gehrke, J. (2003). Query processing for sensor networks. In ICDCSW ’03: Proceedings of the 23rd international conference on distributed computing systems.Google Scholar
  23. Zimmer, D., & Unland, R. (1999). On the semantics of complex events in active database management systems. In ICDE ’99: Proceedings of the 15th international conference on data engineering (p. 392). New York, NY, USA: IEEE Computer Society.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  1. 1.Location-based Services, Fraunhofer Institute for Software and Systems Engineering (ISST) and Freie Universität BerlinBerlinGermany
  2. 2.Institute of Information SystemsHumboldt-Universität zu BerlinBerlinGermany

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