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Issue No.01 - January (2012 vol.24)
pp: 141-155
Yanming Nie , Sch. of Comput. Sci., Northwestern Polytech. Univ., Xi'an, China
Despite its promise, RFID technology presents numerous challenges, including incomplete data, lack of location and containment information, and very high volumes. In this work, we present a novel data inference and compression substrate over RFID streams to address these challenges. Our substrate employs a time-varying graph model to efficiently capture possible object locations and interobject relationships such as containment from raw RFID streams. It then employs a probabilistic algorithm to estimate the most likely location and containment for each object. By performing such online inference, it enables online compression that recognizes and removes redundant information from the output stream of this substrate. We have implemented a prototype of our inference and compression substrate and evaluated it using both real traces from a laboratory warehouse setup and synthetic traces emulating enterprise supply chains. Results of a detailed performance study show that our data inference techniques provide high accuracy while retaining efficiency over RFID data streams, and our compression algorithm yields significant reduction in output data volume.
radiofrequency identification, data compression, graph theory, inference mechanisms, online inference, SPIRE, data inference, data compression, RFID streams, RFID technology, time-varying graph, Radiofrequency identification, Data processing, Image color analysis, Supply chain management, Probabilistic logic, Data compression, supply-chain management., RFID, data streams, data cleaning, compression
Yanming Nie, "SPIRE: Efficient Data Inference and Compression over RFID Streams", IEEE Transactions on Knowledge & Data Engineering, vol.24, no. 1, pp. 141-155, January 2012, doi:10.1109/TKDE.2011.79
[1] RFID: Applications, Security, and Privacy, S. Garfinkel and B. Rosenberg, eds. Addison-Wesley, 2005.
[2] Y. Yao and J. Gehrke, "Query Processing in Sensor Networks," Proc. Conf. Innovative Data Systems Research (CIDR), 2003.
[3] S. Madden, M.J. Franklin, J.M. Hellerstein, and W. Hong, "The Design of an Acquisitional Query Processor for Sensor Networks," Proc. ACM SIGMOD Int'l Conf. Management of Data, pp. 491-502, 2003.
[4] A. Deshpande, C. Guestrin, S. Madden, J.M. Hellerstein, and W. Hong, "Model-Driven Data Acquisition in Sensor Networks," Proc. Int'l Conf. Very Large Data Bases (VLDB), pp. 588-599, 2004.
[5] B. Feder, "Despite Wal-Mart's Edict, Radio Tags Will Take Time," http:/, Dec. 2004.
[6] S.R. Jeffery, G. Alonso, M.J. Franklin, W. Hong, and J. Widom, "Declarative Support for Sensor Data Cleaning," Proc. Int'l Conf. Pervasive Computing (Pervasive), pp. 83-100, 2006.
[7] K. Finkenzeller, RFID Handbook: Radio Frequency Identification Fundamentals and Applications. John Wiley and Sons, 1999.
[8] C. Floerkemeier and M. Lampe, "Issues with RFID Usage in Ubiquitous Computing Applications," Proc. Int'l Conf. Pervasive Computing (Pervasive), pp. 188-193, 2004.
[9] B. Violino, "RFID Opportunities and Challenges," 537, 2011.
[10] M.J. Franklin, S.R. Jeffery, S. Krishnamurthy, F. Reiss, S. Rizvi, E. Wu, O. Cooper, A. Edakkunni, and W. Hong, "Design Considerations for High Fan-in Systems: The HiFi Approach," Proc. Conf. Innovative Data Systems Research (CIDR), pp. 290-304, 2005.
[11] S.R. Jeffery, M.N. Garofalakis, and M.J. Franklin, "Adaptive Cleaning for RFID Data Streams," Proc. Int'l Conf. Very Large Data Bases (VLDB), pp. 163-174, 2006.
[12] M.N. Garofalakis, K.P. Brown, M.J. Franklin, J.M. Hellerstein, D.Z. Wang, E. Michelakis, L. Tancau, E.W. 0002, S.R. Jeffery, and R. Aipperspach, "Probabilistic Data Management for Pervasive Computing: The Data Furnace Project," IEEE Data Eng. Bull., vol. 29, no. 1, pp. 57-63, Mar. 2006.
[13] C. Ré, J. Letchner, M. Balazinska, and D. Suciu, "Event Queries on Correlated Probabilistic Streams," Proc. ACM SIGMOD Int'l Conf. Management of Data, pp. 715-728, 2008.
[14] H. Gonzalez, J. Han, X. Li, and D. Klabjan, "Warehousing and Analyzing Massive RFID Data Sets," Proc. Int'l Conf. Data Eng. (ICDE), p. 83, 2006.
[15] F. Wang and P. Liu, "Temporal Management of RFID Data," Proc. Int'l Conf. Very Large Data Bases (VLDB), pp. 1128-1139, 2005.
[16] EPCglobal Inc., "EPCglobal Tag Data Standards Version 1.3," http:/, Mar. 2006.
[17] Y. Nie, R. Cocci, Y. Diao, and P. Shenoy, "Spire: Efficient Data Interpretation and Compression over Rfid Streams," technical report, Dept. of Computer Science, Univ. of Massachusetts Amherst, , 2009.
[18] R.S. Barga, J. Goldstein, M.H. Ali, and M. Hong, "Consistent Streaming through Time: A Vision for Event Stream Processing," Proc. Conf. Innovative Data Systems Research (CIDR), pp. 363-374, 2007.
[19] W.M. White, M. Riedewald, J. Gehrke, and A.J. Demers, "What Is 'Next' in Event Processing," Proc. ACM SIGMOD-SIGACT-SIGART Symp. Principles of Database Systems (PODS), pp. 263-272, 2007.
[20] N. Khoussainova, M. Balazinska, and D. Suciu, "Towards Correcting Input Data Errors Probabilistically Using Integrity Constraints," Proc. ACM Int'l Workshop Data Eng. for Wireless and Mobile Access (MobiDE), pp. 43-50, 2006.
[21] T. Tran, C. Sutton, R. Cocci, Y. Nie, Y. Diao, and P. Shenoy, "Probabilistic Inference over Rfid Streams in Mobile Environments," Proc. Int'l Conf. Data Eng. (ICDE), pp. 1096-1107, 2009.
[22] S.S. Chawathe, V. Krishnamurthy, S. Ramachandran, and S.E. Sarma, "Managing RFID Data," Proc. Int'l Conf. Very Large Data Bases (VLDB), pp. 1189-1195, 2004.
[23] E. Welbourne, N. Khoussainova, J. Letchner, Y. Li, M. Balazinska, G. Borriello, and D. Suciu, "Cascadia: A System for Specifying, Detecting, and Managing Rfid Events," Proc. Int'l Conf. Mobile Systems, Applications, and Services (MobiSys), pp. 281-294, 2008.
[24] J. Rao, S. Doraiswamy, H. Thakkar, and L.S. Colby, "A Deferred Cleansing Method for RFID Data Analytics," VLDB '06: Proc. 32nd Int'l Conf. Very Large Data Bases, pp. 175-186, 2006.
[25] J. Xie, J. Yang, Y. Chen, H. Wang, and P.S. Yu, "A Sampling-Based Approach to Information Recovery," Proc. Int'l Conf. Data Eng. (ICDE), pp. 476-485, 2008.
[26] B. Kanagal and A. Deshpande, "Online Filtering, Smoothing and Probabilistic Modeling of Streaming Data," Proc. Int'l Conf. Data Eng. (ICDE), pp. 1160-1169, 2008.
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