ABSTRACT
Radio Frequency Identification (RFID) is an automatic identification (Auto-ID) Technology, which is most commonly used now days in healthcare for tracking and identifying objects. In the context of assistive environment, statistical query analysis over the history of Data generated from RFID Applications as well as real time monitoring of the patients or the elderly people (people who need assistance) are really important. But Data generated from these types of healthcare applications can be very large, if each individual object becomes RFID-Tagged. As a result, the RFID technology is also imposing a greater challenge to provide efficient query responses over these Data. In this paper, we show how to apply traditional Data Warehousing techniques to model these massive amounts of RFID Data. In short, we describe how to construct an RFID Warehouse so that important query analyses can be performed very efficiently. We also show how to process a continuous stream of RFID Data to answer real time queries using Sliding Window techniques. By the help of using synthetic Datasets, we conclude that querying over Data Warehouse is much faster than traditional Relational DBMS. We also find that the aforesaid performance improvement is expected to be much higher as the size of the Dataset increases.
- "Study on the requirements and options for RFID application in healthcare: Identifying areas for Radio Frequency Identification deployment in health care delivery: A review of relevant literature," 2009.Google Scholar
- B. S. Ashar and A. Ferriter, "Radiofrequency identification technology in health care: benefits and potential risks," JAMA: The Journal of the American Medical Association, vol. 298, Nov. 2007, pp. 2305--2307.Google ScholarCross Ref
- E. Becker, V. Metsis, R. Arora, J. Vinjumur, Y. Xu, and F. Makedon, "SmartDrawer: RFID-based smart medicine drawer for assistive environments," Proceedings of the 2nd International Conference on PErvsive Technologies Related to Assistive Environments, Corfu, Greece: ACM, 2009, pp. 1--8. Google ScholarDigital Library
- A. Cangialosi, J. Monaly, and S. Yang, "Leveraging RFID in hospitals: Patient life cycle and mobility perspectives," Communications Magazine, IEEE, vol. 45, 2007, pp. 18--23. Google ScholarDigital Library
- H. Gonzalez, Jiawei Han, Xiaolei Li, and D. Klabjan, "Warehousing and Analyzing Massive RFID Data Sets," Data Engineering, 2006. ICDE '06. Proceedings of the 22nd International Conference on, 2006, p. 83. Google ScholarDigital Library
- F. Wang and P. Liu, "Temporal management of RFID data," Proceedings of the 31st international conference on Very large data bases, Trondheim, Norway: VLDB Endowment, 2005, pp. 1128--1139. Google ScholarDigital Library
- R. Derakhshan, M. Orlowska, and Xue Li, "RFID Data Management: Challenges and Opportunities," RFID, 2007. IEEE International Conference on, 2007, pp. 175--182.Google Scholar
- J. Gray, A. Bosworth, A. Lyaman, and H. Pirahesh, "Data cube: a relational aggregation operator generalizing GROUP-BY, CROSS-TAB, and SUB-TOTALS," Data Engineering, 1996. Proceedings of the Twelfth International Conference on, 1996, pp. 152--159. {9} A. Melski, L. Thoroe, and M. Schumann, "Managing RFID data in supply chains," International Journal of Internet Protocol Technology, vol. 2, 2007, pp. 176--189. Google ScholarDigital Library
- A. Melski, L. Thoroe, and M. Schumann, "Managing RFID data in supply chains," International Journal of Internet Protocol Technology, vol. 2, 2007, pp. 176--189. Google ScholarDigital Library
- K. Cotterill, "Distributed Patient-Drug Tracking in Healthcare Organizations," Solution White-Paper, Bonsai Development Corporation, 2004--2005.Google Scholar
- R. Weinstein, "RFID: a technical overview and its application to the enterprise," IT Professional, vol. 7, 2005, pp. 27--33. Google ScholarDigital Library
- J. Woods, "RFID Enables Sensory Network Strategies to Transform Industries,", 2005.Google Scholar
- R. A. Elmasri and S. B. Navathe, Fundamentals of Database Systems, Addison-Wesley Longman Publishing Co., Inc., 1999. Google ScholarDigital Library
- B. Babcock, S. Babu, M. Datar, R. Motwani, and J. Widom, "Models and issues in data stream systems," Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems, Madison, Wisconsin: ACM, 2002, pp. 1--16. Google ScholarDigital Library
Index Terms
- Applying data warehousing technique in pervasive assistive environment
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