OLAP over Continuous Domains via Density-Based Hierarchical Clustering

  • Michelangelo Ceci
  • Alfredo Cuzzocrea
  • Donato Malerba
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6882)


In traditional OLAP systems, roll-up and drill-down operations over data cubes exploit fixed hierarchies defined on discrete attributes that play the roles of dimensions, and operate along them. However, in recent years, a new tendency of considering even continuous attributes as dimensions, hence hierarchical members become continuous accordingly, has emerged mostly due to novel and emerging application scenarios like sensor and data stream management tools. A clear advantage of this emerging approach is that of avoiding the beforehand definition of an ad-hoc discretization hierarchy along each OLAP dimension. Following this latest trend, in this paper we propose a novel method for effectively and efficiently supporting roll-up and drill-down operations over OLAP data cubes with continuous dimensions via a density-based hierarchical clustering algorithm. This algorithm allows us to hierarchically cluster together dimension instances by also taking fact-table measures into account in order to enhance the clustering effect with respect to the possible analysis. Experiments on two well-known multidimensional datasets clearly show the advantages of the proposed solution.


Cluster Algorithm Association Rule Range Query Data Cube Continuous Domain 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Michelangelo Ceci
    • 1
  • Alfredo Cuzzocrea
    • 2
  • Donato Malerba
    • 1
  1. 1.Dipartimento di InformaticaUniversitá degli Studi di Bari “Aldo Modo”BariItaly
  2. 2.ICAR-CNR and University of CalabriaRende, CosenzaItaly

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