Optimizing Evaluated Preference Data in Relational Database Using Query Optimization
Preference-aware queries are needed to be processed closer to the DBMS. Preference database tuples are elaborated from the preference-aware relational data model and the queries with the preferences are processed using an extended algebra. Query optimization strategies are provided for extended query plan based on the set of algebraic properties and cost model. Further illustration of an query execution algorithm that blends preference evaluation with query execution, simultaneously utilizing the native query engine. The framework and methods have been implemented in a prototype system, PrefDB. Transparent and efficient evaluations of preferential queries of a relational DBMS are allowed by PrefDB. 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.
Fathima Sanjas .M,Shoba Rani .P