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Data Interpolating over RFID Data Streams for Missed Readings

  • Yingyuan Xiao
  • Tao Jiang
  • Yukun Li
  • Guangquan Xu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7901)

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.

Keywords

l RFID data stream missed readings data cleaning data interpolating method 

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References

  1. 1.
    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)Google Scholar
  2. 2.
    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)CrossRefGoogle Scholar
  3. 3.
    Rahm, E., Hong, H.: Data cleaning: Problems and current approaches. IEEE Data Engineering Bulletin 23(4), 3–13 (2000)Google Scholar
  4. 4.
    Sarndal, C.E., Swensson, B., Wretman, J.: Model assisted survey sampling. Springer (2003)Google Scholar
  5. 5.
    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)Google Scholar
  6. 6.
    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)Google Scholar
  7. 7.
    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)CrossRefGoogle Scholar
  8. 8.
    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)Google Scholar
  9. 9.
    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)Google Scholar
  10. 10.
    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)Google Scholar
  11. 11.
    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)Google Scholar
  12. 12.
    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)Google Scholar
  13. 13.
    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)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Yingyuan Xiao
    • 1
  • Tao Jiang
    • 1
  • Yukun Li
    • 1
  • Guangquan Xu
    • 2
  1. 1.Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology, Key Laboratory of Computer Vision and SystemTianjin University of TechnologyChina
  2. 2.School of Computer Science and TechnologyTianjin UniversityTianjinChina

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