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

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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.

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Notes

  1. The active database community often refers to “Event—Condition—Action (ECA)‘’ rules. That is, events occur and if they follow some condition some action is taken.

References

  • 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.

  • Arasu, A., Babu, S., & Widom, J. (2006). The cql continuous query language: Semantic foundations and query execution. The VLDB Journal, 15(2), 121–142.

    Article  Google Scholar 

  • 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.

  • 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 

  • Chakravarthy, S., & Mishra, D. (1994). SNOOP: An expressive event specification language for active databases. Journal of Data and Knowledge Engineering, 14(1).

  • 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 

  • Doolin, D., & Sitar, N. (2005). Wireless sensors for wildfire monitoring. In Proceedings of the SPIE symposium on smart structures & materials/ NDE 2005.

  • 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).

  • 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 

  • 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.

    Chapter  Google Scholar 

  • GTZ-Potsdam (2007). German indonesian tsunami early warning system. http://www.gitews.org/.

  • 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 

  • 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.

  • 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.

    Article  Google Scholar 

  • 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.

  • Muehl, G., Fiege, L., & Pietzuch, P. R. (2006). Distributed event-based systems. New York: Springer.

    Google Scholar 

  • 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).

  • 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).

    Article  Google Scholar 

  • 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 

  • 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.

  • 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).

  • Yao, Y., & Gehrke, J. (2003). Query processing for sensor networks. In ICDCSW ’03: Proceedings of the 23rd international conference on distributed computing systems.

  • 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 

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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.

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Authors and Affiliations

  1. Location-based Services, Fraunhofer Institute for Software and Systems Engineering (ISST) and Freie Universität Berlin, Steinplatz 2, 10623, Berlin, Germany

    Agnès Voisard

  2. Institute of Information Systems, Humboldt-Universität zu Berlin, Spandauer Strasse 1, 10178, Berlin, Germany

    Holger Ziekow

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  1. Agnès Voisard
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  2. Holger Ziekow
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Correspondence to Holger Ziekow.

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Voisard, A., Ziekow, H. Modeling trade-offs in the design of sensor-based event processing infrastructures. Inf Syst Front 14, 317–330 (2012). https://doi.org/10.1007/s10796-010-9248-y

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