A Partial View Materialization Approach in Data Warehouse Environment

Abstract

In a data warehouse environment (DWE), users access very large databases to carry out strategic analysis for maintaining business competitiveness by executing OLAP queries. Therefore, efficient query processing becomes a critical issue. The storage space of the dedicated OLAP server is smaller than the total size of all data cubes usually. That is, only parts of the data cubes can be stored. Materialized views selection is the most important decisions in building a Data Warehouse. At the heart of all OLAP or multidimensional data analysis applications is the ability to simultaneously aggregate across many sets of dimensions. Computing multidimensional aggregates is a performance bottleneck for these applications. In this paper, we address an approach that is combined materialized views selection and computing the views for implementation of materialized views. The Partial View Materialization Approach includes two phases of views selection and views materialization. It also includes the advantages of Extended Reverse Progressive View Materialization Algorithm (ERPVMA) and Overlap Method, so the approach can effectively improve the data warehouse performance.