Achieving query optimization using sparsity management in OLAP system

Data Warehouses are increasing their data volume at an accelerated rate; high disk space consumption; slow query response time and complex database administration are common problems in these environments. The lack of a proper data model and an adequate architecture specifically targeted towards these environments are the root causes of these problems. Inefficient management of stored data includes duplicate values at column level and poor management of data sparsity which derives from a low data density, and affects the final size of Data Warehouses. It finds that Relational technology and the Relational Model are not the best techniques for managing duplicates and data sparsity. The novelty of this research is to compare some data models considering their data density and their data sparsity management to optimize Data Warehouse environments. In this research paper various techniques for query performance optimization have been explored and a close association of its conceptual aspects with Oracle Warehouse Builder is mapped.