||As the mobile communication devices become popular, getting the location data of various objects is more convenient than before. Mobile groups that exhibit spatial and temporal proximities can be used for marketing, criminal detection, and ecological studies, just to name a few. Although nowadays the most advanced position equipments are capable of achieving a high accuracy with the measurement error less than 10 meters, they are still expensive. Positioning equipments using different technologies incur different amount of measurement errors ranging from 10 meters to a few hundred meters. In this thesis, we examine the impact of measurement errors on the accuracy of identified valid mobile groups and apply Kalman Filter and RTS smoothing as the one-way and two-way correction to correct the measurement data. In most settings, the corrected location data yield more accurate valid mobile groups. However, when the measurement error is small and users do not make abrupt change in their speed, mining mobile groups directly on the measurement data, however, yield better results.|
||[BH97] Brown,R.G., and Hwang,P.Y.C.(1997). Introduction to Random Signals and Applied Kalman Filtering : with MATLAB Exercises and Solutions(Third ed.): Wiley & Sons, Inc.|
[BWHJ94] B.Hofmann-Wellenhof, H.Lichtenegger, and J.Collins. Global Positioning System:Theory and Practice, volume I. Springer-Verlag Wien New York, third revised edition,April, 1994.
[CK00] Guanling Chen and David Kotz. A Survey of Context-Aware Mobile Computing Research. Dartmouth Computer Science Technical Report TR2000-381, Department of Computer Science, Dartmouth College, 2000.
[GKT02] G. Giaglis, P. Kourouthanasis, and A. Tsamakos. Mobile Commerce: Technology, Theory, and Applications, chapter Towards a Classification Network for Mobile Location Services. Idea Group Publishing, 2002.
[GS00] V. Guralnik, J. Srivastava. “Event Detection from Time Series Data.” Proceedings of ACM International Conference on Knowledge Discovery and Data Mining (KDD2000), 2000.
[HH04] San-Yih Hwang and Chin-Ming Hwang, “Mining Mobile Group Patterns: A Trajectory-based Approach”, master thesis, National Sun Yan-sen University, Department of Information management, Jul. 2004.
[HLCL05] Ying-Han Liu, ”Mining Mobile Group Patterns: A Trajectory-based Approach”, master thesis, National Sun Yan-sen University, Department of Information management, Jul. 2004.
[Kal60] Kalman, R.E., 1960; “A new approach to linear filtering and prediction problems”, Trans. ASME, Series D, J. Basic Eng., V. 82, March, pp. 35 - 45
[May79] Maybeck, Peter S. 1979.Stochastic Models, Estimation, and Control, Volume 1, Chapter 1, Academic Press, Inc.
[Med69] S. Meditch, Stochastic optimal Linear Estimation and Control, New York: McGraw-Hill, 1969.
[Mur04] K. Murphy. Computer Science and Artificial Intelligence Laboratory in MIT. http://www.cs.ubc.ca/~murphyk/Software/Kalman/
kalman.html, June 7, 2004.
[WB04] Greg Welch and Gary Bishop, “An Introduction to Kalman Filter”, Department of Computer Science University of North Carolina at Chapel Hill.
[WLH03] Yida Wang, Ee-Peng Lim, and San-Yih Hwang, “On Mining Group Patterns of Mobile Users.” In Proc. Of the 14th International Conference on Database and Expert Systems Applications-DEXA 2003, Prague, Czech Republic, 1-5 Sep 2003.
[WSCY99] O. Wolfson, A. P. Sistla, S. Chamberlain, Y. Yesha, “Updating and querying databases that track mobile units,” Distributed and Parallel Databases, 1999.
[Zar96] Paul Zarchan. Global Positioning System: Theory and Applications, volume I. American Institute of Aeronautics and Astronautics, 1996.
[ZGL03] Vasileios Zeimpekis, George M. Giaglis, and George Lekakos. “A Taxonomy of Indoor and Outdoor Positioning Techniques for Mobile Location Services,” SIGecom Exchanges, ACM, Volume 3.4, 2003.