Abstract
Sequential pattern mining is an active field in the domain of knowledge discovery and has been widely studied for over a decade by data mining researchers. More and more, with the constant progress in hardware and software technologies, real-world applications like network monitoring systems or sensor grids generate huge amount of streaming data. This new data model, seen as a potentially infinite and unbounded flow, calls for new real-time sequence mining algorithms that can handle large volume of information with minimal scans. However, current sequence mining approaches fail to take into account the inherent multidimensionality of the streams and all algorithms merely mine correlations between events among only one dimension. Therefore, in this paper, we propose to take multidimensional framework into account in order to detect high-level changes like trends. We show that multidimensional sequential pattern mining over data streams can help detecting interesting high-level variations. We demonstrate with empirical results that our approach is able to extract multidimensional sequential patterns with an approximate support guarantee over data streams.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Agrawal, R., Srikant, R.: Mining sequential patterns. In: Proc. 1995 Int. Conf. Data Engineering (ICDE 1995), pp. 3–14 (1995)
Chen, G., Wu, X., Zhu, X.: Sequential pattern mining in multiple streams. In: ICDM, pp. 585–588. IEEE Computer Society, Los Alamitos (2005)
Chen, Y., Dong, G., Han, J., Wah, B.W., Wang, J.: Multi-dimensional regression analysis of time-series data streams. In: VLDB, pp. 323–334 (2002)
Chi, Y., Wang, H., Yu, P.S., Muntz, R.R.: Moment: Maintaining closed frequent itemsets over a stream sliding window. In: Proceedings of the 4th IEEE International Conference on Data Mining (ICDM 2004), pp. 59–66, Brighton, UK (2004)
Giannella, G., Han, J., Pei, J., Yan, X., Yu, P.: Mining frequent patterns in data streams at multiple time granularities. In: Kargupta, H., Joshi, A., Sivakumar, K., Yesha, Y. (eds.) Next Generation Data Mining. MIT Press, Cambridge (2003)
Han, J., Chen, Y., Dong, G., Pei, J., Wah, B.W., Wang, J., Cai, Y.D.: Stream cube: An architecture for multi-dimensional analysis of data streams. Distributed and Parallel Databases 18(2), 173–197 (2005)
Li, H.-F., Lee, S.Y., Shan, M.-K.: An efficient algorithm for mining frequent itemsets over the entire history of data streams. In: Proceedings of the 1st International Workshop on Knowledge Discovery in Data Streams, Pisa, Italy (2004)
Manku, G., Motwani, R.: Approximate frequency counts over data streams. In: Proceedings of the 28th International Conference on Very Large Data Bases (VLDB 2002), pp. 346–357, Hong Kong, China (2002)
Marascu, A., Masseglia, F.: Mining sequential patterns from data streams: a centroid approach. J. Intell. Inf. Syst. 27(3), 291–307 (2006)
Pei, J., Han, J., Mortazavi-Asl, B., Wang, J., Pinto, H., Chen, Q., Dayal, U., Hsu, M.-C.: Mining sequential patterns by pattern-growth: The prefixspan approach. IEEE Transactions on Knowledge and Data Engineering 16(10) (2004)
Pinto, H., Han, J., Pei, J., Wang, K., Chen, Q., Dayal, U.: Multi-dimensional sequential pattern mining. In: CIKM, pp. 81–88 (2001)
Plantevit, M., Choong, Y.W., Laurent, A., Laurent, D., Teisseire, M.: M\(^{\mbox{2}}\)SP: Mining sequential patterns among several dimensions. In: Jorge, A.M., Torgo, L., Brazdil, P.B., Camacho, R., Gama, J. (eds.) PKDD 2005. LNCS (LNAI), vol. 3721, pp. 205–216. Springer, Heidelberg (2005)
Raïssi, C., Poncelet, P., Teisseire, M.: Need for speed: Mining sequential patterns in data streams. In: BDA (2005)
Yu, C.-C., Chen, Y.-L.: Mining sequential patterns from multidimensional sequence data. IEEE Transactions on Knowledge and Data Engineering 17(1), 136–140 (2005)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Raïssi, C., Plantevit, M. (2008). Mining Multidimensional Sequential Patterns over Data Streams. In: Song, IY., Eder, J., Nguyen, T.M. (eds) Data Warehousing and Knowledge Discovery. DaWaK 2008. Lecture Notes in Computer Science, vol 5182. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85836-2_25
Download citation
DOI: https://doi.org/10.1007/978-3-540-85836-2_25
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-85835-5
Online ISBN: 978-3-540-85836-2
eBook Packages: Computer ScienceComputer Science (R0)