A method of A-BAT algorithm based query optimization for crowd sourcing system

In the field of database administration query optimization is one of the refinement processes. In recent years, huge volumes of data are flooded from different resources, which make query optimization, a difficult task for the researchers. In the crowd sourcing, environment query optimization is the biggest problem. The client is simply required to post an SQL-like subject, and the framework assumes the main issue of organizing the inquiry; execution setup is generated and in the crowd sourcing market places the evaluation plan evaluated. In order to retrieve data fast and reduce query processing time, Query optimization is badly required. In order to optimize the queries, Meta heuristic techniques are used. In this proposed paper, preprocessing method is used to mine the information from the Crowd. The Original population based ABC algorithm has low convergence speed. In this paper a novel A-BAT algorithm is proposed, which highly improve convergence speed, accuracy and Latency. This algorithm uses a Random walk phase. The proposed algorithm had better optimization accuracy, convergence rate, and robustness.