Learning Machine Implementation for Big Data Analytics, Challenges and SolutionsAhmed N AL-Masri* and Manal M Nasir
American University in the Emirates, DIAC, P.O. Box: 31624, United Arab Emirates
- *Corresponding Author:
- Ahmed N AL-Masri
American University in the Emirates, DIAC
P.O. Box: 31624, United Arab Emirates
E-mail: [email protected]
Received Date: December 28, 2015 Accepted Date: February 16, 2016 Published Date: February 22, 2016
Citation: AL-Masri AN, Nasir MM (2016) Learning Machine Implementation for Big Data Analytics, Challenges and Solutions. Journal of Data Mining in Genomics & Proteomics 7:190. doi:10.4172/2153-0602.1000190
Copyright: © 2016 AL-Masri AN, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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.