Abstract
While tracing objects or analyzing human activities with RFID data sets, the quality of RFID data is a crucial aspect. The raw RFID data streams, however, tend to be noisy, including missed readings and unreliable readings. Traditional data cleaning tends to focus on a small set of well-defined tasks, including transformation, matching, and duplicate elimination. In this paper, we focus on exploring efficient methods for interpolating missed readings. We propose a novel probabilistic interpolating method and three novel deterministic interpolating methods based on time interval, containment relationship and inertia of objects, respectively. We conduct extensive experiments and the experimental results demonstrate the feasibility and effectiveness of our methods.
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References
Chaves, L.W.F., Buchmann, E., Böhm, K.: Finding Misplaced Items in Retail by Clustering RFID Data. In: Proc. of the 13th International Conference on Extending Database Technology. ACM Press (2010)
Floerkemeier, C., Lampe, M.: Issues with RFID usage in ubiquitous computing applications. In: Ferscha, A., Mattern, F. (eds.) PERVASIVE 2004. LNCS, vol. 3001, pp. 188–193. Springer, Heidelberg (2004)
Rahm, E., Hong, H.: Data cleaning: Problems and current approaches. IEEE Data Engineering Bulletin 23(4), 3–13 (2000)
Sarndal, C.E., Swensson, B., Wretman, J.: Model assisted survey sampling. Springer (2003)
Franklin, M.J., Jeffery, S.R., Krishnamurthy, S.: Design Considerations for High Fan-in Systems: The HiFi Approach. In: Proc. of the 2nd Biennial Conference on Innovative Data Systems Research, pp. 290–304 (2005)
Jeffery, S.R., Alonso, G., Franklin, M.J., Wei, H., Widom, J.: Progressive skyline computation in database systems. In: Proc. of the 22nd International Conference on Data Engineering (2006)
Jeffery, S.R., Alonso, G., Franklin, M.J., Hong, W., Widom, J.: Declarative Support for Sensor Data Cleaning. In: Fishkin, K.P., Schiele, B., Nixon, P., Quigley, A. (eds.) PERVASIVE 2006. LNCS, vol. 3968, pp. 83–100. Springer, Heidelberg (2006)
Jeffery, S.R., Garofalakis, M., Franklin, M.J.: Adaptive Cleaning for RFID Data Streams. In: Proc. of the 32nd International Conference on Very Large Data Bases, pp. 163–174 (2006)
Kanagal, B., Deshpande, A.: Online Filtering, Smoothing and Probabilistic Modeling of Streaming Data. In: Proc. of the 5th ACM International Workshop on Data Engineering for Wireless and Mobile Access, pp. 43–50 (2006)
Khoussainova, N., Balazinska, M., Suciu, D.: Towards Correcting Input Data Errors Probabilistically Using Integrity Constraints. In: Proc. of the 24th International Conference on Data Engineering, pp. 1160–1169 (2008)
Rao, J., Doraiswamy, S., Thakkar, H., Colby, L.S.: A Deferred Cleansing Method for RFID Data Analytics. In: Proc. of the 32nd International Conference on Very Large Data Bases, pp. 175–186 (2006)
Chen, H., Ku, W.S., Wang, H., Sun, M.T.: Leveraging Spatio-Temporal Redundancy for RFID Data Cleansing. In: Proc. of the ACM International Conference on Management of Data, pp. 51–62 (2010)
Jiang, T., Xiao, Y., Wang, X., Li, Y.: Leveraging Communication Information among Readers for RFID Data Cleaning. In: Wang, H., Li, S., Oyama, S., Hu, X., Qian, T. (eds.) WAIM 2011. LNCS, vol. 6897, pp. 201–213. Springer, Heidelberg (2011)
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Xiao, Y., Jiang, T., Li, Y., Xu, G. (2013). Data Interpolating over RFID Data Streams for Missed Readings. In: Gao, Y., et al. Web-Age Information Management. WAIM 2013. Lecture Notes in Computer Science, vol 7901. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39527-7_26
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DOI: https://doi.org/10.1007/978-3-642-39527-7_26
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-39526-0
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