Description
Nowadays, most organizations deal with complex data that have different formats and come from different sources. The XML formalism is evolving and becoming a promising solution for modeling and warehousing these data in decision support systems. Nevertheless, classical OLAP tools still are not capable of analyzing such data. In this article, we associate OLAP and data mining to cope with advanced analysis on complex data. We provide a generalized OLAP operator, called OpAC, based on the AHC. OpAC is adapted for all types of data, since it deals with data cubes modeled within XML. Our operator enables significant aggregates of facts expressing semantic similarities. Evaluation criteria of aggregates’ partitions are proposed in order to assist the choice of the best partition. Furthermore, we developed a Web application for our operator. We also provide performance experiments and drive a case study on XML documents dealing with the breast cancer research domain.