Bridging Physical and Virtual Worlds: Complex Event Processing for RFID Data Streams

  • Fusheng Wang
  • Shaorong Liu
  • Peiya Liu
  • Yijian Bai
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3896)


Advances of sensor and RFID technology provide significant new power for humans to sense, understand and manage the world. RFID provides fast data collection with precise identification of objects with unique IDs without line of sight, thus it can be used for identifying, locating, tracking and monitoring physical objects. Despite these benefits, RFID poses many challenges for data processing and management: i) RFID observations contain duplicates, which have to be filtered; ii) RFID observations have implicit meanings, which have to be transformed and aggregated into semantic data represented in their data models; and iii) RFID data are temporal, streaming, and in high volume, and have to be processed on the fly. Thus, a general RFID data processing framework is needed to automate the transformation of physical RFID observations into the virtual counterparts in the virtual world linked to business applications. In this paper, we take an event-oriented approach to process RFID data, by devising RFID application logic into complex events. We then formalize the specification and semantics of RFID events and rules. We demonstrate that traditional ECA event engine cannot be used to support highly temporally constrained RFID events, and develop an RFID event detection engine that can effectively process complex RFID events. The declarative event-based approach greatly simplifies the work of RFID data processing, and significantly reduces the cost of RFID data integration.


Complex Event Temporal Constraint Event Graph Event Instance Interval Constraint 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    EPC Tag Data Standards Version 1.1. Technical report, EPCGlobal Inc (April 2004)Google Scholar
  2. 2.
    Wang, F., Liu, P.: Temporal Management of RFID Data. In: VLDB (2005)Google Scholar
  3. 3.
    RFID, FORUM Report. 2004- Forum.pdf (2004),
  4. 4.
    Chakravarthy, S., Mishra, D.: Snoop: an Expressive Event Specification Language for Active Databases. Data Knowl. Eng. 14(1), 1–26 (1994)CrossRefGoogle Scholar
  5. 5.
    Motakis, I., Zaniolo, C.: Formal Semantics for Composite Temporal Events in Active Database Rules. Journal of Systems Integration 7(3/4), 291–325 (1997)CrossRefGoogle Scholar
  6. 6.
    The METRO Group Future Store Initiative.,
  7. 7.
    Chakravarthy, S., Krishnaprasad, V., Anwar, E., Kim, S.-K.: Composite Events for Active Databases: Semantics, Contexts and Detection. In: VLDB, pp. 606–617 (1994)Google Scholar
  8. 8.
    Widom, J., Ceri, S.: Active Database Systems: Triggers and Rules For Advanced Database Processing. Morgan Kaufmann, San Francisco (1996)Google Scholar
  9. 9.
    Gehani, N.H., Jagadish, H.V., Shmueli, O.: Composite Event Specification in Active Databases: Model & Implementation. In: VLDB (1992)Google Scholar
  10. 10.
    Palmer, M.: Seven Principles of Effective RFID Data Management. docs/ articles/7principles rfid mgmnt.pdf (August 2004),
  11. 11.
    Gupta, A., Srivastava, M.: Developing Auto-ID Solutions using Sun Java System RFID Software. sjsrfid/RFID.html (October 2004),
  12. 12.
    Bornhoevd, C., Lin, T., Haller, S., Schaper, J.: Integrating Automatic Data Acquisition with Business Processes - Experiences with SAP’s Auto-ID Infrastructure. In: VLDB, pp. 1182–1188 (2004)Google Scholar
  13. 13.
  14. 14.
    WebSphere RFID Premises Server (December 2004),
  15. 15.
    Sybase RFID Solutions (2005),
  16. 16.
  17. 17.
    Franklin, M.J., Jeffery, S.R., Krishnamurthy, S., Reiss, F., Rizvi, S., Wu, E., Cooper, O., Edakkunni, A., Hong, W.: Design Considerations for High Fan-In Systems: The HiFi Approach. In: CIDR, pp. 290–304 (2005)Google Scholar
  18. 18.
    Rizvi, S., Jeffery, S.R., Krishnamurthy, S., Franklin, M.J., Burkhart, N., Edakkunni, A., Liang, L.: Events on the Edge. In: SIGMOD, pp. 885–887 (2005)Google Scholar
  19. 19.
    Gatziu, S., Dirtrich, K.R.: Detecting Composite Events in Active Databases Using Petri Nets. In: Workshop on Research Issues in Data Engineering: Active Database Systems (1994)Google Scholar
  20. 20.
    Mansouri-Samani, M., Sloman, M.: GEM: a Generalized Event Monitoring Language for Distributed Systems. Distributed Systems Engineering 4(2), 96–108 (1997)CrossRefGoogle Scholar
  21. 21.
    Liu, G., Mok, A., Konana, P.: A Unified Approach for Specifying Timing Constraints and Composite Events in Active Real-Time Database Systems. In: RTAS (1998)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Fusheng Wang
    • 1
  • Shaorong Liu
    • 2
  • Peiya Liu
    • 1
  • Yijian Bai
    • 2
  1. 1.Integrated Data Systems DepartmentSiemens Corporate ResearchPrincetonUSA
  2. 2.Computer Science DepartmentUniversity of California, Los AngelesLos AngelesUSA

Personalised recommendations