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Conference Papers Year : 2008

Mining Multidimensional Sequential Patterns over Data Streams

Chedy Raïssi
Marc Plantevit

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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Dates and versions

lirmm-00324432 , version 1 (07-09-2022)

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Chedy Raïssi, Marc Plantevit. Mining Multidimensional Sequential Patterns over Data Streams. DaWaK 2008 - 10th International Conference on Data Warehousing and Knowledge Discovery, Sep 2008, Turin, Italy. pp.263-272, ⟨10.1007/978-3-540-85836-2_25⟩. ⟨lirmm-00324432⟩
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