Systematic survey on evolution of machine learning for big data

摘要

Advanced data processing techniques with massive and high dimensional data, dramatically increased storage capability and complex data formats cause the Big data. In this realm, to solve the various issues of computational time to extract the valuable information without sensitive information loss, the Big data need modern advanced technologies and/or techniques. To overcome those problems, a novel and rapidly expanding research domain have been recently proposed: Machine Learning. Generally Machine learning algorithms have been considered to learn and find useful and valuable information from large volumes of data. The goal of this paper is to build the effective universal architecture which defines the quality and durability of a system software. The paper intends to add to the Systematic Literature Review (SLR) to help specialists who are endeavoring to contribute around there. The principle target of this audit is to deliberately recognize and dissect the as of late distributed research subjects identified with Machine learning in big data as to research action, utilized apparatuses and systems, proposed methodologies and spaces. The connected strategy in SLR depends on three chose electronic databases proposed by (Kitchenham and Charters, 2007).