Learning Machine Implementation for Big Data Analytics, Challenges and Solutions

Abstract

Big Data analytics is one of the great challenges for Learning Machine (LM) algorithms because most real-life applications involve a massive information or big data knowledge base. By contrast, an Artificial Intelligent (AI) system with a data knowledge base should be able to compute the result in an accurate and fast manner. This study focused on the challenges and solutions of using with Big Data. Data processing is a mandatory step to transform unstructured Big Data into a meaningful and optimized data set in any LM module. However, an optimized data set must be deployed to support a distributed processing and real-time application. This work also reviewed the technologies currently used in Big Data analysis and LM computation and emphasized that the viability of using different solutions for certain applications could increase LM performance. The new development, especially in cloud computing and data transaction speed, offers significant advantages to the practical use of AI applications.