Abstract
In the context of mobile computing, a special sequential pattern, moving sequential pattern that reflects the moving behavior of mobile users attracted researchers’ interests recently. While there have been a number of efficient moving sequential pattern mining algorithms reported, this paper concentrates on the maintenance of mined maximal moving sequential patterns. In particular, we developed an incremental approach, where maximal moving sequential patterns are stored in prefix trees, and new moving sequences can be easily combined with the existing patterns. A performance study indicated that the proposed approach performs significantly faster than straightforward approaches that mine from the whole updated database.
Supported by the National High Technology Development 863 Program of China under Grant No. 2002AA4Z3440; the National Grand Fundamental Research 973 Program of China under Grant No. G1999032705; the Foundation of the Innovation Research Institute of PKU-IBM.
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
Ma, S., Wang, T., Tang, S., Yang, D., Gao, J.: Mining Mobile User Maximal Moving Sequential Patterns. Technical report PKU_CS_DB_TR20030601, Department of Computer Science, Peking University, Beijing, China (2003)
Peng, W.-C., Chen, M.-S.: Mining User Moving Patterns for Personal Data Allocation in a Mobile Computing System. In: Proc. of the ICPP Conference, pp. 573–580 (2000)
Shivakumar, N., Jannink, J., Widom, J.: Per-user Profile Replication in Mobile Environments: Algorithms Analysis and Simulation Result. ACM/Baltzer Journal of Mobile Networks and Applications 2(2), 129–140 (1997)
Srikant, R., Agrawal, R.: Mining Sequential Patterns: Generalizations and Performance Improvements. In: Proc. of the 5th EDBT Conference, pp. 3–17 (1996)
Pei, J., Han, J., Mortazavi-Asl, B., et al.: PrefixSpan: Mining Sequential Patterns Efficiently by PrefixProjected Pattern Growth. In: Proc. of the 17th ICDE Conference, pp. 215–224 (2001)
Peng, W.-C., Chen, M.-S.: Developing Data Allocation Schemes by Incremental Mining of User Moving Patterns in a Mobile Computing System. IEEE Transactions on Knowledge and Data Engineering 15(1), 70–85 (2003)
Wong, V.W.S., Leung, V.C.M.: Location Management for Next Generation Personal Communication Networks. IEEE network, special issue on next generation wireless broadband networks 14(5), 8–14 (2000)
Sarasere, A., Omiecinski, E., Navathe, S.: An Efficient Algorithm for Mining Association Rules in Larges Databases. In: Proc. of the 21st VLDB Conference, pp. 432–444 (1995)
SUMATRA: Stanford University Mobile Activity TRAces http://www.db.stanford.edu/sumatra/
Lee, S., Cheung, D.: Maintenance of Discovered Association Rules: When to Update? In: Proc. of SIGMOD DMKD Workshop (1997)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ma, S., Tang, S., Yang, D., Wang, T., Yang, C. (2004). Incremental Maintenance of Discovered Mobile User Maximal Moving Sequential Patterns. In: Lee, Y., Li, J., Whang, KY., Lee, D. (eds) Database Systems for Advanced Applications. DASFAA 2004. Lecture Notes in Computer Science, vol 2973. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24571-1_72
Download citation
DOI: https://doi.org/10.1007/978-3-540-24571-1_72
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-21047-4
Online ISBN: 978-3-540-24571-1
eBook Packages: Springer Book Archive