Abstract
In recent years, there has been an increasing interest in the detection of non-contiguous sequence patterns in data streams. Existing works define a fixed temporal constraint between every pair of adjacent elements of the sequence. While this method is simple and intuitive, it suffers from the following shortcomings: 1)It is difficult for the users who are not domain experts to specify such complex temporal constraints properly; 2)The fixed temporal constraint is not flexible to capture interested patterns hidden in long sequences. In this paper, we introduce a novel type of non-contiguous sequence pattern, named Elastic Temporal Constrained Non-contiguous Sequence Pattern(ETC-NSP). Such a pattern defines an elastic temporal constraint on the sequence, thus is more flexible and effective as opposed to the fixed temporal constraints. Detection of ETC-NSP in data streams is a non-trivial task since a brute force approach is exponential in time. Our method exploits an similarity measurement called Minimal Variance Matching as the basic matching mechanism. To further speed up the monitoring process, we develop pruning strategies which make it practical to use ETC-NSP in streaming environment. Experimental studies show that the proposed method is efficient and effective in detecting non-contiguous sequence patterns from data streams.
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References
Faloutsos, C., Ranganathan, M., Manolopoulos, Y.: Fast Subsequence Matching in Time-Series Databases. In: Proceedings 1994 ACM SIGMOD Conference, Mineapolis, MN (1994)
Mamoulis, N., Yiu, M.L.: Non-contiguous Sequence Pattern Queries. In: Bertino, E., Christodoulakis, S., Plexousakis, D., Christophides, V., Koubarakis, M., Böhm, K., Ferrari, E. (eds.) EDBT 2004. LNCS, vol. 2992, Springer, Heidelberg (2004)
Wang, H., Perng, C.-S., Fan, W., Park, S., Yu, P.S.: Indexing Weighted-Sequences in Large Databases. In: Proceedings of 19th International Conference On Data Engineering (2003)
Latecki, L.J., Megalooikonomou, V., Wang, Q., Lakaemper, R., Ratanamahatana, C.A., Keogh, E.: Elastic Partial Matching of Time Series. In: Jorge, A.M., Torgo, L., Brazdil, P.B., Camacho, R., Gama, J. (eds.) PKDD 2005. LNCS (LNAI), vol. 3721, Springer, Heidelberg (2005)
Hadjieleftheriou, M., Kollios, G., Bakalov, P., Tsotras, V.J.: Complex Spatio-Temporal Pattern Queries. In: Proceedings of the 31st International Conference on Very Large Data Bases (2005)
Hadjieleftheriou, M., Kollios, G., Tsotras, V.J., Gunopulos, D.: Efficient Indexing of Spatiotemporal Objects. In: Jensen, C.S., Jeffery, K.G., Pokorný, J., Šaltenis, S., Bertino, E., Böhm, K., Jarke, M. (eds.) EDBT 2002. LNCS, vol. 2287, Springer, Heidelberg (2002)
Sakurai, Y., Faloutsos, C., Yamamuro, M.: Stream Monitoring under the Time Warping Distance. In: International Conference on Data Engineering(ICDE) (2007)
Keogh, E.J.: Exact indexing of dynamic time warping. In: VLDB 2002 (2002)
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Zuo, X., Zhou, Y., Zhao, C. (2007). Elastic Non-contiguous Sequence Pattern Detection for Data Stream Monitoring. In: Yin, H., Tino, P., Corchado, E., Byrne, W., Yao, X. (eds) Intelligent Data Engineering and Automated Learning - IDEAL 2007. IDEAL 2007. Lecture Notes in Computer Science, vol 4881. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-77226-2_61
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DOI: https://doi.org/10.1007/978-3-540-77226-2_61
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-77225-5
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