Title: Fragmenting very large XML data warehouses via K-means clustering algorithm

Authors: Alfredo Cuzzocrea, Jerome Darmont, Hadj Mahboubi

Addresses: ICAR-CNR and University of Calabria, Via P. Bucci, 41C, Rende, 87036 Cosenza, Italy. ' University of Lyon (ERIC Lyon 2), 5 avenue Pierre Mendes-France, 69676 Bron Cedex, France. ' University of Lyon (ERIC Lyon 2), 5 avenue Pierre Mendes-France, 69676 Bron Cedex, France

Abstract: XML data sources are gaining popularity in the context of Business Intelligence and On-Line Analytical Processing (OLAP) applications, due to the amenities of XML in representing and managing complex and heterogeneous data. However, XML-native database systems currently suffer from limited performance, both in terms of volumes of manageable data and query response time. Therefore, recent research efforts are focusing on horizontal fragmentation techniques, which are able to overcome the above limitations. However, classical fragmentation algorithms are not suitable to control the number of originated fragments, which instead plays a critical role in data warehouses. In this paper, we propose the use of the K-means clustering algorithm for effectively and efficiently supporting the fragmentation of very large XML data warehouses. We complement our analytical contribution with a comprehensive experimental assessment where we compare the efficiency of our proposal against existing fragmentation algorithms.

Keywords: XML data warehousing; horizontal fragmentation; clustering; K-means; business intelligence; online analytical processing; OLAP.

DOI: 10.1504/IJBIDM.2009.029076

International Journal of Business Intelligence and Data Mining, 2009 Vol.4 No.3/4, pp.301 - 328

Published online: 03 Nov 2009 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article