Optimization Design of Query Processing Performance Using Appropriate Materialized View Selection & Preservation

Abstract: In this paper, we describe the effective design of materialized view selection and preservation in a data warehousing system. This design implementation aims to aid users in retrieving data effectively for business analysis. The skeleton design of this data warehousing system employs the dimensional modeling concepts of snowflake as well as star schemes. Here, some of frequently accessed queries are stored in various user files on which we apply materialized view selection process to create materialized views in order to minimize the query processing cost. A cost analysis model was developed to enable the estimate the total cost and benefit involved in selecting each materialized view. For effective materialized views selection and preservation methodology, WMVS and PMV algorithms has been implemented.
This algorithm takes into account an effective cost variables associated with the materialized views Selection and preservation method which includes query access frequencies, materialized view access frequencies, query processing costs, materialized view access cost ,query storage cost , materialized view storage cost and the availability of the system’s storage. The algorithm has been applied to dummy tables containing student information to create cost effective set of materialized summary views, , thereby resulting in an efficient data warehousing system where storage and query processing of the system is optimized.

Keywords: Materialized View, View-selection, Access Frequency, storage cost, Materialized view preservation, query processing