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CAMS: OLAPing Multidimensional Data Streams Efficiently

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Data Warehousing and Knowledge Discovery (DaWaK 2009)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 5691))

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Abstract

In the context of data stream research, taming the multidimensionality of real-life data streams in order to efficiently support OLAP analysis/mining tasks is a critical challenge. Inspired by this fundamental motivation, in this paper we introduce CAMS (C ube-based A cquisition model for M ultidimensional S treams), a model for efficiently OLAPing multidimensional data streams. CAMS combines a set of data stream processing methodologies, namely (i) the OLAP dimension flattening process, which allows us to obtain dimensionality reduction of multidimensional data streams, and (ii) the OLAP stream aggregation scheme, which aggregates data stream readings according to an OLAP-hierarchy-based membership approach. We complete our analytical contribution by means of experimental assessment and analysis of both the efficiency and the scalability of OLAPing capabilities of CAMS on synthetic multidimensional data streams. Both analytical and experimental results clearly connote CAMS as an enabling component for next-generation Data Stream Management Systems.

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Author information

Authors and Affiliations

  1. ICAR-CNR and University of Calabria, Italy

    Alfredo Cuzzocrea

Authors
  1. Alfredo Cuzzocrea
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Editor information

Editors and Affiliations

  1. Department of Computer Science, Aalborg University, Selma Lagerlöfsvej 300, 9220, Aalborg Ø, Denmark

    Torben Bach Pedersen

  2. IBM India Research Lab, Plot No. 4, Block C, Institutional Area, Vasant Kunj, 110 070, New Delhi, India

    Mukesh K. Mohania

  3. Institute of Software Technology and Interactive Systems, Vienna University of Technology, Favoritenstr. 9-11/188, 1040, Wien, Austria

    A Min Tjoa

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© 2009 Springer-Verlag Berlin Heidelberg

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Cuzzocrea, A. (2009). CAMS: OLAPing Multidimensional Data Streams Efficiently. In: Pedersen, T.B., Mohania, M.K., Tjoa, A.M. (eds) Data Warehousing and Knowledge Discovery. DaWaK 2009. Lecture Notes in Computer Science, vol 5691. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03730-6_5

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  • DOI: https://doi.org/10.1007/978-3-642-03730-6_5

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-03729-0

  • Online ISBN: 978-3-642-03730-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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