DATABASE CLUSTERING AND DATA WAREHOUSING

Abstract

Due to the complexity of real-world applications, the number of databases and the volume of data have increased tremendously. Discovering qualitative and quantitative patterns form databases in such a distributed information-providing environment has been recognized as a challenging task. In response to such a demand, data mining and data warehousing techniques are emerging to extract the previously unknown and potentially useful knowledge to provide better decision support. This paper presents a mechanism called Markov Model Mediators (MMMs) to facilitate the understanding of the data warehouse schemas/views and the improvement of the query processing performance by analyzing and discovering the summarized knowledge at the database level. Simulation results Show that the data mining process leads to a better federation of data warehouses and reduces the cost of query processing. To illustrate these benefits, our approach has been implemented and a simple example and several experiments on real databases are presented.