View materialization using fuzzy MAX–MIN composition with association rule mining (VMFCA)

Materialized view creation is an intrinsic requirement for the relational model to speed up the query processing majorly for large data centric applications. Different queries are generated by the system, and these are to be answered in real time, that is, immediately. Several studies have been performed over the time to address these speed-related issues, and the formation of materialized view is found to be one of the effective ways of executing this. Materialized view creation using statistical methodologies is one of the common practices. However, the existing statistical methods follow linear function which has some shortcomings. In this research work, the problems of linear function-based methodologies are mitigated using fuzzy logic-based solution which incorporates nonlinearity. Fuzzy logic is used here to analyze the association among the attributes in the given query set, and after that a modified association rule mining technique is applied to construct the materialized view from the analyzed attribute set. Experimental results are carried out on the real-life dataset using the proposed methodology, and the result shows better performance in terms of hit ratio and less response time over the existing methodologies.