Work place: Information Technology Dept., Faculty of Computers and Information, Mansoura University, Mansoura, 35511, Egypt
E-mail: melmogy@mans.edu.eg
Website:
Research Interests: Computer systems and computational processes, Computational Learning Theory, Computer Vision, Pattern Recognition, Data Structures and Algorithms
Biography
Mohammed Elmogy is an associate professor at Information Technology Dept., Faculty of Computers and Information, Mansoura University, Egypt. He had received his B.Sc. and M.Sc. from Faculty of Engineering, Mansoura University, Egypt. He had received his Ph.D. from Informatics Department, MIN Faculty, Hamburg University, Germany in 2010. He has authored/coauthored over 70 research publications in peer-reviewed reputed journals, book chapters, and conference proceedings. He has served as a reviewer for various international journals. His current research interests are Computer Vision, Machine Learning, Pattern Recognition, and Biomedical Engineering.
By Mai Abdrabo Mohammed Elmogy Ghada Eltaweel Sherif Barakat
DOI: https://doi.org/10.5815/ijitcs.2016.08.01, Pub. Date: 8 Aug. 2016
The world has been drowned by floods of data due to technological development. Consequently, the Big Data term has gotten the expression to portray the gigantic sum. Different sorts of quick data are doubling every second. We have to profit from this enormous surge of data to convert it to knowledge. Knowledge Discovery (KDD) can enhance detecting the value of Big Data based on some techniques and technologies like Hadoop, MapReduce, and NoSQL. The use of Big Data value is critical in different fields.
This survey discusses the expansion of data that led the world to Big Data expression. Big Data has distinctive characteristics as volume, variety, velocity, value, veracity, variability, viscosity, virality, ambiguity, and complexity. We will describe the connection between Big Data and KDD techniques to reach data value. Big Data applications that are applied by big organizations will be discussed. Characteristics of big data will be introduced, which represent a significant challenge for Big Data management. Finally, some of the important future directions in Big Data field will be presented.
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