Abstract
Recently, it has become possible to collect large amounts of trajectory data of moving objects by using sensor networks. To manage such trajectory data, we have developed a distributed trajectory database composed of a server and many sensor nodes deployed over wide areas. The server manages the trajectory data of each moving object by using indices. However, since each sensor node cannot send trajectory data to the server all the time, the server does not always manage indices for the current trajectory data. In other words, the server is delayed in answering queries for current data because it has to forward each query to the sensor nodes to answer them. This is defined as a delay problem. To avoid this problem, we propose a pattern-based predictive indexing method for the database to answer queries in real time. This method uses past motion patterns of moving objects to predict the future locations of moving objects. In this paper, we describe the method and evaluate it with practical trajectory data. We conclude that the technique can predict the future locations of moving objects well enough in real time and show optimal parameters for prediction.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Laube, P., Imfeld, S.: Analyzing relative motion within groups of trackable moving point objects. In: Egenhofer, M.J., Mark, D.M. (eds.) GIScience 2002. LNCS, vol. 2478, pp. 132–144. Springer, Heidelberg (2002)
Vazirgiannis, M., Wolfson, O.: A spatio temporal model and language for moving objects on road networks. In: Jensen, C.S., Schneider, M., Seeger, B., Tsotras, V.J. (eds.) SSTD 2001. LNCS, vol. 2121, pp. 20–35. Springer, Heidelberg (2001)
Papadias, D., Zhang, J., Mamoulis, N., Tao, Y.: Query processing in spatial network databases. In: Proceedings of the 29th VLDB Conference, Berlin, German, pp. 12–23 (2003)
Yanagisawa, Y., Akahani, J.: Shape-based similarity query for trajectory of mobile objects. In: Chen, M.-S., Chrysanthis, P.K., Sloman, M., Zaslavsky, A. (eds.) MDM 2003. LNCS, vol. 2574, pp. 63–77. Springer, Heidelberg (2003)
Saltenis, S., Jensen, C.S., Leutenegger, S.T., Lopez, M.A.: Indexing the positions of continuously moving objects. In: Proceedings of SIGMOD Conference, pp. 331–342 (2000)
Guttman, O.: R-trees: a dynamic index structure for spatial searching. In: Proceedings of SIGMOD, Conference, pp. 47–57 (1984)
Zhang, Q., Lin, X.: Clustering moving objects for spatio-temporal selectivity estimation. In: Proceedings of the fifteenth conference on Australasian database, vol. 27, pp. 123–130 (2004)
Kollios, G., Tsotras, V.J., Gunopulos, D., Delis, A., Hadjieleftheriou, M.: Indexing animated objects using spatiotemporal access methods. Knowledge and Data Engineering 13, 758–777 (2001)
Agarwal, P.K., Arge, L., Erickson, J.: Indexing moving points. In: Proceedings of Symposium on Principles of Database Systems, pp. 175–186 (2000)
Tao, Y., Sun, J., Papadias, D.: Selectivity estimation for predictive spatio-temporal queries. In: Proceedings of International Conference on Data Engineering, pp. 417–428 (2003)
Choi, Y.J., Chung, C.W.: Selectivity estimation for spatio-temporal queries to moving objects. In: Proceedings of the 2002 ACM SIGMOD international conference on Management of data, Madison, Wisconsin, USA. ACM SIGMOD international conference on Management of data table of contents, pp. 440–451. ACM Press, New York (2002)
Hadjieleftheriou, M., Kollios, G., Tsotras, V., Gunopulos, D.: On-line discovery of dense areas in spatio-temporal databases. In: Hadzilacos, T., Manolopoulos, Y., Roddick, J., Theodoridis, Y. (eds.) SSTD 2003. LNCS, vol. 2750, pp. 306–324. Springer, Heidelberg (2003)
Hadjieleftheriou, M., Kollios, G., Tsotras, V.J.: Performance evaluation of spatio-temporal selectivity estimation techniques. In: Proceedings of 15th International Conference on Scientific and Statistical Database Management, Cambridge, Massachusetts, USA, pp. 202–211. IEEE Computer Society, Los Alamitos (2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Katsuda, K., Yanagisawa, Y., Satoh, T. (2005). A Pattern-Based Predictive Indexing Method for Distributed Trajectory Databases. In: Kim, C. (eds) Information Networking. Convergence in Broadband and Mobile Networking. ICOIN 2005. Lecture Notes in Computer Science, vol 3391. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30582-8_78
Download citation
DOI: https://doi.org/10.1007/978-3-540-30582-8_78
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-24467-7
Online ISBN: 978-3-540-30582-8
eBook Packages: Computer ScienceComputer Science (R0)