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Event-Based Compression and Mining of Data Streams

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Knowledge-Based Intelligent Information and Engineering Systems (KES 2008)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5178))

Abstract

An innovative event-based data stream compression and mining model is presented in this paper. The main novelty of our approach with respect to traditional data stream compression approaches relies on the semantics of the application in driving the compression process by identifying ”interested” events occurring in the unbounded stream. This puts the basis for a novel class of intelligent applications over data streams where the knowledge on actual streams is integrated with and correlated to the knowledge related to expired events that are considered critical for the target application scenario.

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Author information

Authors and Affiliations

  1. ICAR Inst. and University of Calabria, Italy

    Alfredo Cuzzocrea

  2. Dept. of Computer Science & Engineering, The University of Texas at Arlington,  

    Sharma Chakravarthy

Authors
  1. Alfredo Cuzzocrea
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  2. Sharma Chakravarthy
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Editor information

Ignac Lovrek Robert J. Howlett Lakhmi C. Jain

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© 2008 Springer-Verlag Berlin Heidelberg

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Cuzzocrea, A., Chakravarthy, S. (2008). Event-Based Compression and Mining of Data Streams. In: Lovrek, I., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2008. Lecture Notes in Computer Science(), vol 5178. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85565-1_83

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  • DOI: https://doi.org/10.1007/978-3-540-85565-1_83

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-85564-4

  • Online ISBN: 978-3-540-85565-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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