Managing Reduction in Multidimensional Databases

Dealing with large amount of data has always been a key focus of the Multidimensional Database (MDB) community, especially in the current era when data volume increases more and more rapidly. In this paper, we outline a conceptual modeling solution allowing reducing data in MDBs. A MDB after reduction is modeled with multiple states. Each state is valid during a period of time and aggregates data from a more recent state. We propose three alternatives of reduced MDB modeling at the logical level: (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 MDB into separate tables, each table includes data from one or several states. We evaluate query execution efficiency in MDBs with and without data reduction. The result shows data reduction is an interesting solution, since it significantly decreases execution costs by 98.96% during our experimental assessments.