Abstract
A more effective method for massive data query optimization using HDFS and the Bio-inspired algorithm. Big Data configuration and query optimization are the two phases of the process. To remove redundant data, the input data is first per-processed using HDFS. Then, utilizing entropy calculation, features like closed frequent pattern, support, and confidence are extracted and managed. The Bio-inspired Horse Herd approach is used to group pertinent information based on this outcome. In the second step, the Big Data queries are used to obtain the same features. The optimized query is then located using the Bio-inspired technique, and the similarity assessment procedure is run. The proposed algorithm, according to this research, outperforms other ones that is unique in use. It is challenging to determine the veracity of this claim without more information regarding the experimental setup and the precise measures employed to assess the algorithm's effectiveness. Furthermore, it is unknown how the proposed algorithm stacks up against other cutting-edge query optimization methods. Finally, the assess has efficiency of using this method, more optimistic query achieved and comparison analysis are proved.
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