Olap over continuous domains via density-based hierarchical clustering

Abstract. 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 hier-archical members become continuous accordingly, has emerged mostly due to novel and emerging application scenarios like sensor and data stream manage-ment tools. A clear advantage of this emerging approach is that of avoiding the beforehand definition of an ad-hoc discretization hierarchy along each OLAP di-mension. 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.