Efficient Context-Aware Nested Complex Event Processing over RFID Streams

  • Shanglian PengEmail author
  • Jia He
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9998)


With large scale of utilization of monitoring devices such as RFID, sensors and mobile phones, events are generated in a high-speed fashion. Decisions should be made in real time during business processes. Complex Event Processing (CEP) has become increasingly important for tracking and monitoring anomalies and trends in event streams. Nested event detection of RFID event stream is one of the most import class of queries. Current optimization of nested RFID event detection mainly considers caching intermediate results to reduce re-computation of similar results for nested subexpression. In this paper, we use context information of an RFID scenario to optimize nested event detection. We formalize context of an RFID scenario as spatial and temporal constraints and transform these constraints into rules over a nested NFA. Further, we present rewriting context rules to optimize nested event query plan. Experimental results show that with context information introduced, response time had been reduced greatly compared with counterpart methods.


Context aware Complex event processing Nested pattern NFA Data stream RFID 


  1. 1.
    Luckham, D.C.: The Power of Events: An Introduction to Complex Event Processing in Distributed Enterprise Systems. Addison-Wesley, Boston (2002)Google Scholar
  2. 2.
    Wu, E., Diao, Y.L., Rizvi, S.: High-performance complex event processing over streams. In: SIGMOD, pp. 407–418 (2006)Google Scholar
  3. 3.
    Zhang, H., Diao, Y., Immerman, N.: Recognizing patterns in streams with imprecise timestamps. PVLDB 3(1), 244–255 (2010)Google Scholar
  4. 4.
    Nie, Y., Cocci, R., Cao, Z., Diao, Y., Shenoy, P.J.: SPIRE: efficient data inference and compression over RFID streams. IEEE Trans. Knowl. Data Eng. 24(1), 141–155 (2012)CrossRefGoogle Scholar
  5. 5.
    Zhang, H., Diao, Y., Immerman, N.: On complexity and optimization of expensive queries in complex event processing. In: International Conference on Management of Data, SIGMOD 2014, Snowbird, UT, USA, 22–27 June 2014, pp. 217–228 (2014)Google Scholar
  6. 6.
    Mei, Y., Madden, S.: Zstream: a cost-based query processor for adaptively detecting composite events. In: SIGMOD (2009)Google Scholar
  7. 7.
    Brenna, L., Demers, A., Gehrke, J., et al.: Cayuga: a high-performance event processing engine (demo). In: SIGMOD (2007)Google Scholar
  8. 8.
    Demers, A., Gehrke, J., Hong, M., et al.: Cayuga: a general purpose event monitoring system. In: CIDR (2007)Google Scholar
  9. 9.
    Barga, R.S., Goldstein, J., Ali, M.H., Hong, M.: Consistent streaming through time: a vision for event stream processing. In: CIDR 2007, Third Biennial Conference on Innovative Data Systems Research, Asilomar, CA, USA, 7–10 January 2007, Online Proceedings, pp. 363–374 (2007)Google Scholar
  10. 10.
    Liu, M., Rundensteiner, E.A., Dougherty, D.J., Gupta, C., Wang, S., Ari, I., Mehta, A.: High-performance nested CEP query processing over event streams. In: Proceedings of the 27th International Conference on Data Engineering, ICDE, 11–16 April 2011, Hannover, Germany, pp. 123–134 (2011)Google Scholar
  11. 11.
    Liu, M., Ray, M., Rundensteiner, E.A., Dougherty, D.J., Gupta, C., Wang, S., Ari, I., Mehta, A.: Processing nested complex sequence pattern queries over event streams. In: Proceedings of the Seventh International Workshop on Data Management for Sensor Networks, DMSN 2010, pp. 14–19. ACM, New York (2010)Google Scholar
  12. 12.
    Ray, M., Liu, M., Rundensteiner, E.A., Dougherty, D.J., Gupta, C., Wang, S., Mehta, A., Ari, I.: Optimizing complex sequence pattern extraction using caching. In: Workshops Proceedings of the 27th International Conference on Data Engineering, ICDE, 11–16 April 2011, Hannover, Germany, pp. 243–248 (2011)Google Scholar
  13. 13.
    Liu, M., Ray, M., Zhang, D., Rundensteiner, E.A., Dougherty, D.J., Gupta, C., Wang, S., Ari, I.: Realtime healthcare services via nested complex event processing technology. In: 15th International Conference on Extending Database Technology, EDBT 2012, Berlin, Germany, 27–30 March 2012, Proceedings, pp. 622–625 (2012)Google Scholar
  14. 14.
    Ray, M., Rundensteiner, E.A., Liu, M., Gupta, C., Wang, S., Ari, I.: High-performance complex event processing using continuous sliding views. In: Joint 2013 EDBT/ICDT Conferences, EDBT 2013 Proceedings, Genoa, Italy, 18–22 March 2013, pp. 525–536 (2013)Google Scholar
  15. 15.
    Chakravarthy, S., Krishnaprasad, V., Anwar, E., Kim, S.: Composite events for active databases: semantics, contexts and detection. In: VLDB, pp. 606–617 (1994)Google Scholar
  16. 16.
    Gatsiu, S., Dittrich, K.R.: Events in an active object-oriented database system. In: International Conference on Rules in Database Systems, pp. 23–39 (1993)Google Scholar
  17. 17.
    Hirzel, M.: Partition and compose: parallel complex event processing. In: DEBS, pp. 191–200. Citeseer (2012)Google Scholar
  18. 18.
    Wu, S., Kumar, V., Wu, K.L., Ooi, B.C.: Parallelizing stateful operators in a distributed stream processing system: how, should you and how much? In: Proceedings of the 6th ACM International Conference on Distributed Event-Based Systems, pp. 278–289. ACM (2012)Google Scholar
  19. 19.
    Schneider, S., Hirzel, M., Gedik, B., Wu, K.L.: Auto-parallelizing stateful distributed streaming applications. In: Proceedings of the 21st International Conference on Parallel Architectures and Compilation Techniques, pp. 53–64. ACM (2012)Google Scholar
  20. 20.
    Etzion, O., Magid, Y., Rabinovich, E., Skarbovsky, I., Zolotorevsky, N.: Context-based event processing systems. In: Helmer, S., Poulovassilis, A., Xhafa, F. (eds.) Reasoning in Event-Based Distributed Systems. SCI, vol. 347, pp. 257–278. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  21. 21.
    Etzion, O., Niblett, P.: Event Processing in Action, 1st edn. Manning Publications Co., Greenwich (2010)Google Scholar
  22. 22.
    Taylor, K., Leidinger, L.: Ontology-driven complex event processing in heterogeneous sensor networks. In: Antoniou, G., Grobelnik, M., Simperl, E., Parsia, B., Plexousakis, D., De Leenheer, P., Pan, J. (eds.) ESWC 2011, Part II. LNCS, vol. 6644, pp. 285–299. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  23. 23.
    Teymourian, K., Paschke, A.: Enabling knowledge-based complex event processing. In: Proceedings of the 2010 EDBT/ICDT Workshops, EDBT 2010, pp. 37:1–37:7. ACM, New York (2010)Google Scholar
  24. 24.
    Cao, K., Wang, Y., Wang, F.: Context-aware distributed complex event processing method for event cloud in internet of things. Adv. Inf. Sci. Serv. Sci. 5(8), 1212 (2013)Google Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.School of Information Science and TechnologySouthwest Jiaotong UniversityChengduChina
  2. 2.College of Computer ScienceChengdu University of Information TechnologyChengduChina

Personalised recommendations