Abstract-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.

Frequent item sets mining plays an important role in association rules mining. Over the years, a variety of algorithms for finding frequent item sets in very large transaction databases have been developed. Therefore, a number of methods have been proposed recently to discover approximate frequent item sets. This paper proposes an efficient SMine (Sorted Mine) Algorithm for finding frequent item sets. This proposed method reduces the number of scans in the database. Our proposed SMine algorithm works well based on graph construction. At last we performed an experiment on a real dataset to test the run time of our proposed algorithm. The experiment showed that it was efficient for mining datasets.

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Many advances in computer systems and IT infrastructures increases the risks associated with the use of these technologies. Specifically, intrusion into computer systems by unauthorized users is a growing problem and it is very challenging to detect. Intrusion detection technologies are therefore becoming extremely important to improve the overall security of computer systems. In the past decades, most of the intrusion detection systems designed suffer from the problem of high false negative and low efficiency rate. A powerful intrusion detection system (IDS) should be implemented to solve these issues and it is necessary to collect, reduce and analysis the data automatically. The integration of machine learning and artificial intelligence techniques serves this purpose in this paper. A use of particle swarm optimization (PSO) selects the optimal number of clusters and the integration of k-means based artificial neural network (ANN) achieves maximum efficiency when the number of clusters selected optimally. The proposed IDS are t bested with NSL-KD dataset and the experiment result shows the significance of the proposed IDS.