Mining Multidimensional Sequential Patterns Over Data Streams

Marc Plantevit 1 Chedy Raïssi 1
1 TATOO - Fouille de données environnementales
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
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.
Type de document :
Communication dans un congrès
Il-Yeol Song and Johann Eder and Tho Manh Nguyen. DaWaK'08: Data Warehousing and Knowledge Discovery, Sep 2008, Turin, Italy. Springer, 5182, pp.263-272, 2008, Lecture Notes in Computer Science
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https://hal-lirmm.ccsd.cnrs.fr/lirmm-00324432
Contributeur : Marc Plantevit <>
Soumis le : jeudi 25 septembre 2008 - 07:55:03
Dernière modification le : jeudi 24 mai 2018 - 15:59:22

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  • HAL Id : lirmm-00324432, version 1

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Marc Plantevit, Chedy Raïssi. Mining Multidimensional Sequential Patterns Over Data Streams. Il-Yeol Song and Johann Eder and Tho Manh Nguyen. DaWaK'08: Data Warehousing and Knowledge Discovery, Sep 2008, Turin, Italy. Springer, 5182, pp.263-272, 2008, Lecture Notes in Computer Science. 〈lirmm-00324432〉

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