OLAP over Continuous Domains via Density-Based Hierarchical Clustering

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