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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.
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https://hal-lirmm.ccsd.cnrs.fr/lirmm-00324432
Contributor : Marc Plantevit <>
Submitted on : Thursday, September 25, 2008 - 7:55:03 AM
Last modification on : Friday, February 14, 2020 - 2:04:08 PM

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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. DaWaK'08: Data Warehousing and Knowledge Discovery, Sep 2008, Turin, Italy. pp.263-272. ⟨lirmm-00324432⟩

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