Effective OLAP mining of evolving data marts

Abstract

Organizations have been used decisions support systems to help them to understand and to predict interesting business opportunities over their huge databases also known as data marts. OLAP tools have been used widely for retrieving information in a summarized way (cube-like) by employing customized cubing methods. The majority of these cubing methods suffer from being just data-driven oriented and not discovery-driven ones. Data marts grow quite fast, so an incremental OLAP mining process is a required and desirable solution for mining evolving cubes. In order to present a solution that covers the previous mentioned issues, we propose a cube-based mining method which can compute an incremental cube, handling concept hierarchy modeling, as well as, incremental mining of multidimensional and multilevel association rules. The evaluation study using real and synthetic datasets demonstrates that our approach is an effective OLAP mining method of evolving data marts.