A Knowledge Based Approach for Query Optimization in Preferential Mapping Relational Databases

Abstract

Relational query databases provide a high level declarative interface to access data stored in relational databases. Two key components of the query evaluation component of a SQL database system are the query optimizer and the query execution engine. System R optimization framework since this was a remarkably elegant approach that helped fuel much of the subsequent work in optimization. Transparent and efficient evaluations of preferential queries are allowed by relational database systems. This results in experimenting extensive evaluation on two real world data sets which illustrates the feasibility and advantages of the framework. Early pruning of results based on score or confidence during query processing are enabled by combining the prefer operator with the rank and rank join operators. During preference evaluation, both the conditional and the scoring part of a preference are used. The conditional part acts as a soft constraint that determines which records are scored without disqualifying any duplicates from the query result. To introduce a preferences mapping relational data model that extends database with profile preferences for query optimizing and an extended algebra that captures the essence of processing queries with ranking method. Based on a set of algebraic properties and a cost model that to propose, to provide several query optimization strategies for extended query plans. To describe a query execution algorithm that blends preference evaluation with query execution, while making effective use of the native query engine.