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.

Effectively and efficiently supporting roll-up and drill-down OLAP operations over continuous dimensions via hierarchical clustering

CECI, MICHELANGELO
;
MALERBA, Donato
2015

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.
File in questo prodotto:
File Dimensione Formato  
Ceci2015_Article_EffectivelyAndEfficientlySuppo.pdf

non disponibili

Tipologia: Documento in Versione Editoriale
Licenza: NON PUBBLICO - Accesso privato/ristretto
Dimensione 1.69 MB
Formato Adobe PDF
1.69 MB Adobe PDF   Visualizza/Apri   Richiedi una copia

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11586/36313
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 42
  • ???jsp.display-item.citation.isi??? 24
social impact