Mining Multidimensional Sequential Patterns over Data Streams

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Mining Multidimensional Sequential Patterns over Data Streams
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 variation...
Chedy Raïssi, Marc Plantevit
Added 19 Oct 2010
Updated 19 Oct 2010
Type Conference
Year 2008
Authors Chedy Raïssi, Marc Plantevit
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