Journal of Information Processing Systems

Korea Information Processing Society (한국정보처리학회)
권호 표지
DOI QR코드

DOI QR Code

Distance Functions to Detect Changes in Data Streams

  • Bud Ulziitugs (Dept. of Computer Science, Kongju National University) ;
  • Lim, Jong-Tae (Dept. of Computer Science, Kongju National University)
  • Published : 2006.03.01
Download PDF
⟨ Previous Next ⟩

Abstract

One of the critical issues in a sensor network concerns the detection of changes in data streams. Recently presented change detection schemes primarily use a sliding window model to detect changes. In such a model, a distance function is used to compare two sliding windows. Therefore, the performance of the change detection scheme is greatly influenced by the distance function. With regard to sensor nodes, however, energy consumption constitutes a critical design concern because the change detection scheme is implemented in a sensor node, which is a small battery-powered device. In this paper, we present a comparative study of various distance functions in terms of execution time, energy consumption, and detecting accuracy through simulation of speech signal data. The simulation result demonstrates that the Euclidean distance function has the highest performance while consuming a low amount of power. We believe our work is the first attempt to undertake a comparative study of distance functions in terms of execution time, energy consumption, and accuracy detection.

Keywords

References

  1. D.Kifer, S.Ben-David, and J.Gehrke. 'Detecting change in data streams' In VLDB, 2004
  2. T.Dasu, S.Krishnan. 'An information-theoretic approach to detecting changes in multi-dimensional data streams', AT&T Labs Research, Duke Uni., 2005
  3. A.Sinha, A.P.Chandrakasan, 'JouleTrack - A web based tool for software energy profiling', Massachusetts Institute of Technology, 2001
  4. http://www.intel.com/design/strong/applnots/sa1100lx
  5. S.S.Chawathe, H.GarciaMolina. 'Meaningful change detection in structured data' In SIGMOD, 1997
  6. Y.Zhu, D.Shasha. 'Efficient elastic burst detection in data streams' In KDD, 2003
  7. V.Ganti, J.Gehrke, R.Ramakrishnan, and W.Y.Loh. 'A framework for measuring differences in data characteristics' Computer and System Sciences, 2002
  8. G.Hulten, L.Spencer, and P.Domingos. 'Mining time changing data streams' In KDD, 2001
  9. C.C.Aggarwal. 'A framework for diagnosing changes in Evolving Data Streams' In SIGMOD, 2003
  10. Rui Xu, Donald Wunsch, 'Survey Of Clustering Algorithms', IEEE Transactions On Neural Networks, Vol. 16, No. 3, May 2005
  11. Rex Min, Manish Bhardwaj, Seong-Hwan Cho, Nathan Ickes, Eugene Shih, Amit Sinha, Alice Wang, and Anantha Chandrakasan, 'Energy-Centric Enabling Technologies For Wireless Sensor Networks' IEEE Wireless Communications, August 2002
  12. Amit Sinha, Alice Wang, and Anantha Chandrakasan 'Energy Scalable System Design' IEEE Transactions On Very Large Scale Integration (VLSI) Systems, Vol. 10, No. 2, April 2002
  13. Amit Sinha Anantha Chandrakasan 'Dynamic Power Management In Wireless Sensor Networks' IEEE Design & Test Of Computers, 2001