Advertisement

Temporal and Location Based RFID Event Data Management and Processing

  • Fusheng WangEmail author
  • Peiya Liu
Chapter

Abstract

Advance of sensor and RFID technology provides 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. RFID data are temporal and history oriented, multi-dimensional, and carrying implicit semantics. Moreover, RFID applications are heterogeneous. RFID data management or data warehouse systems need to support generic and expressive data modeling for tracking and monitoring physical objects, and provide automated data interpretation and processing. We develop a powerful temporal and location oriented data model for modeling and querying RFID data , and a declarative event and rule based framework for automated complex RFID event processing. The approach is general and can be easily adapted for different RFID-enabled applications, thus significantly reduces the cost of RFID data integration.

Keywords

Temporal Constraint Interval Constraint Primitive Event Onboard Sensor Nest Relation 
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.

References

  1. Bai YJ, Wang F, Liu P, Liu S (2007) RFID data processing with a data stream query language. In: ICDE, Istanbul, 15–20 April, pp 1184–1193Google Scholar
  2. Bornhoevd C, Lin C et al (2004) Integrating Automatic Data Acquisition with Business Processes – Experiences with SAP’s Auto-ID Infrastructure. In: VLDB, Toronto, Canada, August 31 – September 3, pp 1182–1188Google Scholar
  3. Chakravarthy S, Mishra D (1994) Snoop: an expressive event specification language for active databases. Data Knowl Eng 14(1):1–26CrossRefGoogle Scholar
  4. Chawathe SS, Krishnamurthy V, Ramachandrany S, Sarma S (2004) Managing RFID data. In: VLDB, Toronto, Canada, August 31 – September 3, pp 1189–1195Google Scholar
  5. EPC Tag Data Standards (TDS) (2008) Version 1.4. EPCGlobal technique report. http://www.epcglobalinc.org/standards/tds/tds_1_4-standard-20080611.pdf. Accessed 30 Nov 2009
  6. EPCGlobal (2009) The EPCglobal network and the global data synchronization network (GDSN). http://www.epcglobalinc.org/about/media_centre/EPCCglobal_and_GDSN_v4_0_Final.pdf. Accessed 30 Nov 2009
  7. Gatziu S, Dirtrich KR (1994) Detecting composite events in active databases using petri nets. In: Workshop on research issues in data engineering: active database systems. Houston, TX, USA, 14–15 February, pp 2–9Google Scholar
  8. Gehani NH Jagadish HV, Shmueli O (1992) Composite event specification in active databases: model and implementation. In: VLDBGoogle Scholar
  9. Gonzalez H, Han J, Li X, Klabjan D (2006) Warehousing and analyzing massive RFID data sets. In: ICDE, Atlanta, Georgia, USA, 3–7 April, pp 83–93Google Scholar
  10. Harrison M (2003) EPC information service – data model and queries. Auto-ID Center Technical ReportGoogle Scholar
  11. Hinze A (2003) Efficient filtering of composite events. In: BNCOD, Coventry, UK, 15–17 July, pp 207–225Google Scholar
  12. IBM Websphere Premise Server (2009) http://www.ibm.com/software/integration/premises_server. Accessed 30 Nov 2009
  13. Lampe M, Flörkemeier C (2004) The smart box application model. International conference on pervasive computing, Linz, Vienna, Austria, 18–23 AprilGoogle Scholar
  14. Lee C, Chung C (2008) Efficient storage scheme and query processing for supply chain management using RFID. In: SIGMODGoogle Scholar
  15. Motakis I, Zaniolo C(1997) Formal semantics for composite temporal events in active database rules. J Syst Integrat 7:291–325CrossRefGoogle Scholar
  16. Oracle Sensor Edge Server (2009) http://www.oracle.com/technology/products/sensor_edge_server. Accessed 30 Nov 2009
  17. Sybase RFID Anywhere (2009) http://www.sybase.com/products/mobilesolutions/rfid_anywhere. Accessed 30 Nov 2009
  18. UCLA WinRFID (2009) http://winmec.ucla.edu/rfid/. Accessed 30 Nov 2009
  19. Wang F, Liu P (2005) Temporal management of RFID data. In: VLDB, Trondheim, Norway, 30 August–2 September, pp 1128–1139Google Scholar
  20. Wu E, Diao Y, Rizvi S (2006) High-performance complex event processing over streams. In: SIGMOD, Chicago, IL, 27–29 June, USA, pp 407–418Google Scholar
  21. Widom J, Ceri S (1996) Active Database Systems: Triggers and Rules for Advanced Database Processing. Morgan Kaufmann, San Francisco, CAGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  1. 1.Center for Comprehensive Informatics, Emory UniversityDruid HillsUSA
  2. 2.Integrated Data Systems DepartmentSiemens Corporate ResearchPrincetonUSA

Personalised recommendations