Abstract
In traditional OLAP systems, roll-up and drill-down operations over data cubes exploit fixed hierarchies defined on discrete attributes, which play the roles of dimensions, and operate along them. New emerging application scenarios, such as sensor networks, have stimulated research on OLAP systems, where even continuous attributes are considered as dimensions of analysis, and hierarchies are defined over continuous domains. The goal is to avoid the prior definition of an ad-hoc discretization hierarchy along each OLAP dimension. Following this research trend, in this paper we propose a novel method, founded on a density-based hierarchical clustering algorithm, to support roll-up and drill-down operations over OLAP data cubes with continuous dimensions. The method hierarchically clusters dimension instances by also taking fact-table measures into account. Thus, we 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.
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Notes
In our implementation, the clustering algorithm used in the third phase is the well-known DBSCAN (Ester et al. 1996) algorithm which performs a density-based clustering.
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Acknowledgements
The authors thank Lynn Rudd for reading through the paper. This work is in partial fulfillment of the requirements of the Italian project VINCENTE PON02_00563_3470993 “A Virtual collective INtelligenCe ENvironment to develop sustainable Technology Entrepreneurship ecosystems”.
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Ceci, M., Cuzzocrea, A. & Malerba, D. Effectively and efficiently supporting roll-up and drill-down OLAP operations over continuous dimensions via hierarchical clustering. J Intell Inf Syst 44, 309–333 (2015). https://doi.org/10.1007/s10844-013-0268-1
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DOI: https://doi.org/10.1007/s10844-013-0268-1