Systematic Approach for Modern data Integration in Hybrid Data System based Distributed Web Information Domain Using Machine Learning Techniques

Abstract

The data integration process evolving from one state to another based on the data systems which are currently in use or may be updated in the future.  The modern systems stored their data bases information in terms of its stream flow and big data formats are handled with data lakes and cloud support for further processing.  The outdated technologies and traditional standards are not feasible to access the modern hybrid data systems if it is not handled with proper approaches and tools.  The proposed data integration components for current use will also focus on the adoption for future trends and techniques which is the main draw back in the existing data integration approaches.  The existing data integration methodologies mainly focus on the data content integration but not with the additional requirements, structure, and proper tool selection which leads the methodologies to deviate it from the goal achievements.  This research article proposes a systematic approach for modern data integration in hybrid data system based distributed web information domain using machine learning techniques.  In future this research paper will be incorporated with the implementation of automata theory based distributed web information integration system.