Materialized views selection using two-phase optimization algorithm

Abstract

A data warehouse (DW) can be defined as a subject-oriented, integrated, nonvolatile and time-variant collection of data, which has value and role for decisionmaking by querying. Queries to DW are critical regarding to their complexity and length. They often access millions of tuples, and involve joins between relations and aggregations. To avoid accessing base tables and increase the speed of queries posed to a DW, we can use some intermediate results from the query processing stored in the DW called materialized views. However, these views have maintenance costs, so materialization of all views is not possible. An important challenge of a DW environment is materialized view selection because we have to realize the trade-off between query processing cost and view maintenance cost. The total cost of allvirtual-views is equal to the query processing cost. Whilst the total cost of allmaterialized-views is the summation of query processing cost and view maintenance cost. However, the query processing cost of this total cost is not significant. A lot of literature has tried to make the total cost lower than all-virtual-views and allmaterialized-view. In this dissertation, we introduce a new approach aimed at solving this challenge based on Two-Phase Optimization (2PO), which is a combination of Simulated Annealing (SA) and Iterative Improvement (II), with the use of Multiple View Processing Plan (MVPP). Our experiments show that our method provides a further improvement in terms of total cost.