Adapting OLAP Analysis to User's Constraints through Semantic Hierarchies

摘要

The objective of this paper is to provide a personalized on-line aggregate operator, namely PRoCK (Personalized Rollup operator with Constrained K-means), based on data mining techniques. The use of data mining techniques, and more precisely constrained K-means clustering method, helps to discover new grouping sets with respect to users requirements. In the context of data warehouses, PRoCK allows to adapt dimension hierarchies to the user constraints. Indeed, applied on a given dimension hierarchy instances, constrained k-means clustering method gives a new natural classification. The obtained clustering results constitute a new hierarchy level semantically richer, namely personalized level on which user may elaborate more sophisticated OLAP analysis. PRoCK is integrated inside Oracle RDBMS (Relational DataBase Management System) and we have carried out some experimentation which validated the relevance of our operator.