Evaluation of aggregated query plans using heuristic approach

In the present era, swarm optimization techniques are quite adaptable towards achieving the flexibilities in terms of expansion of queries achieving the global optimization capabilities. The expansion of queries in general targets to achieve the desired query terms among the available query sets with proper identification of candidate keys. Practically, it is difficult to judge the potentiality of the query terms as well as large queries through traditional computing mechanisms. In such case, it is desirable to employ the optimization techniques to resolve the problems associated with expansion of queries. Also it is essential to make experimentation on parameters associated with the particle swarm optimization techniques. This analysis should focus on query expansion and link to retrieval mechanisms. To enhance the mechanisms of retrieval of information within the stipulated time period, the linked queries can be augmented involving the computational steps along with query expansion techniques. It will be a support to increase the effectiveness of retrieval mechanism of queries and eradicate the anomalies implementing the normalization. The reason of choosing particle swarm optimization in this case is to maintain the members as well as the complete population linked with the retrieval mechanism of queries and to filter the operation obtaining the optimal or near optimal solution. Accordingly, it is essential to update the present generation of particles considering as candidate solutions focusing on velocity, position which may be initialized randomly. During this process there is a great significance of search engine which aims to process huge facts as well as data. With the consistent increase of information, the task of retrieval mechanisms is to aggregate the queries and estimate the performance by simulating the data. In order to achieve better result during retrieval process, particle swarm optimization technique can be adopted to optimize the data and obtain better query formulation. The major merit in this case is its global convergence as well as robustness. Considering the mechanisms associated with data virtualization, it is seen that the logical layer integrates all types of linked data and unifies the same towards real time applications without disturbing the physical storage allocations. Accordingly some related approaches can be adopted towards real time applications. As such one of the most common approaches, heuristic approach has been proposed in this case to evaluate the performance of aggregated query plans.