A Gitbash-Generative Model for an Enhanced Query Processing In a Distributed Database System

Abstract

This research study is directly focused on an enhanced query processing system using GitBash- Deep Generative Model, it is use to improve the performance of big data. Due to Poor query execution plan and absence of an improved aggregate query method which impose threats to qualities of good query processing in distributed databases which also cause memory pressure in a database, inflated central processing unit (CPU) and an overall reduction in concurrency. Also the volume of data use in any organization is progressively increasing in it seize, thereby resulting or demand a large storage location or space system. The availability of intended software that will confront the problem of storage contents and execution plan for query processing is a vital challenge facing big data in a processing system, which can prompt to information loss and missing record of vital document in an enterprises. The study will boost solution and addresses the problem of latencies, inability to transform and store queries, and poor query processing plans for future improvement of distributed databases, using an unsupervised learning approach such as deep generative model. The adopted methodology is dynamic system development method (DSDM). php programming language and mongoDB database is adopted for implementation. The datasets used for the proposed system was generated from e-library server/data.json (query) and is inserted into MongoDB database json format. The datasets are all trained and recognized by the Deep Generative algorithm through the Git Bash server. The proposed system result shows highly increase in the efficiency of management of big data and improved query processing. General Term Query Processing System Keywords Big Data, Deep Generative Model, Query Processing System, Information Management, Gitbash.