Prioritization of complex heterogeneous queries using evolutionary and computational approach

It is well understood that the efficiency of query evaluation can be enhanced through different optimization mechanisms and somehow the query execution plans obtained through static implementation may not be optimal. In such situation, while focusing on query optimization based on virtual server, the strategic implementations many times permits to increase the efficiency of query execution plans. In fact the methodologies associated with query optimization in virtual environment are somehow uncommon as these are linked with search based and schema based applications. Indeed it is clearly known that the optimization criteria can be either static or dynamic. But the parameters associated with queries in virtual platforms can be computed focusing on I/O cost, CPU cost, server/receive response as well as ratio of mechanism towards optimal cost in different situations. Of course, the query execution plans should have provision to be stored in different locations in distributive manner, but many times it will not be feasible to explore and compute the linked query plans associated with large scaled databases. So it is required to focus on specific optimization criteria with proportionate measures to overcome the challenges associated with query optimization. In virtual platform, the sharing of computational resources is not confined only in specific location but it can be provisioned with diversified and distributed locations. Therefore, harmonization of data is very much required during auto access of data based on situation and application.