Multi-Objective Big Data View Materialization Using Improved Strength Pareto Evolutionary Algorithm

Big data refers to the enormous heterogeneous being produced at a brisk pace by large number of diverse generating sources. Since traditional processing technologies are unable process big efficiently, is processed using newer distributed storage and frameworks. view materialization technique queries efficiently on these It generates valuable information, which can be used take timely decisions, especially in cases disasters. As there very views, it not possible materialize all them. Therefore, subset views needs selected for materialization, optimizes query response time given set workload with minimum overheads. This problem, having objectives minimization evaluation cost queries, while simultaneously minimizing update costs materialized has been addressed improved strength pareto evolutionary algorithm (SPEA-2) this paper. The proposed selection algorithm, able compute non-dominated shown perform better that existing algorithms..