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Handling Large Workloads by Profiling and Clustering

  • Matteo Golfarelli
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2737)

Abstract

View materialization is recognized to be one of the most effective ways to increase the Data Warehouse performance; nevertheless, due to the computational complexity of the techniques aimed at choosing the best set of views to be materialized, this task is mainly carried out manually when large workloads are involved. In this paper we propose a set of statistical indicators that can be used by the designer to characterize the workload of the Data Warehouse, thus driving the logical and physical optimization tasks; furthermore we propose a clustering algorithm that allows the cardinality of the workload to be reduced and uses these indicators for measuring the quality of the reduced workload. Using the reduced workload as the input to a view materialization algorithm allows large workloads to be efficiently handled.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Matteo Golfarelli
    • 1
  1. 1.DEISUniversity of BolognaBolognaItaly

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