An efficient query optimization technique in big data using σ\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\sigma$$\end{document}-ANFIS load balancer and CaM-BW optimizer

Big data (BD) is attaining major attention in the information field due to the eruption of data in the preceding decade. Philosophical techniques of “query optimization (QO)” have an essential function in data retrieval as of a BD environment. Numerous cloud-centered distributed data processing platforms were developed to render effective as well as lucrative solutions for BD query optimization. Nevertheless, most techniques brought about higher “energy consumptions (EC)” along with low accuracy level because of the lack of deliberation of energy issues as well as query characteristics, correspondingly. To tackle the issues of query optimization process, this paper proposes an “efficient query optimization (EQO)” utilizing σ\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\sigma$$\end{document} ANFIS load balancer in addition to the CaM-BW optimizer. The proposed technique comprises '2′ phases: (1) BD arrangement and (2) QO. In the initial phase, the BD is arranged by utilizing preprocessing, feature extraction, together with clustering. The MCoV-FCM algorithm takes care of the clustering. In the second phase, the σ\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\sigma$$\end{document} ANFIS load balancer in addition to the CaM-BW optimizer concentrates on disregarding the energy-efficient query plans and enhancing the general query processing performance. Lastly, numerical simulation outcomes are rendered to display the proposed method’s effectiveness.