Managing Reduction in Multidimensional Databases

Abstract

Dealing with large amount of data has always been a key focus of the Data Warehouse (DW) community, especially in the current era when data volume increases more and more rapidly. In this paper, we first describe a conceptual modeling solution allowing reducing data in DWs. Specifically, a DW is modeled with multiple states. Each state is valid during a period of time. Detailed information is aggregated and then deleted in old states over time according to decision-makers' needs. Secondly, we describe several relational modeling alternatives: (i) the flat modeling integrates all states into one single table, (ii) the horizontal modeling converts each state into a fact table and some dimension tables associated with a temporal interval and (iii) the vertical modeling breaks down a reduced DW into separate tables, each table includes data from one or several states. At last, we evaluate query execution efficiency in DWs with and without data reduction. The result shows data reduction is an interesting solution, since it significantly decreases execution costs by 98.96% and it saves up to 95.75% of runtime during our experimental assessments.