Thesis (M.Sc.) - Cairo University - Institute of Statistical Studies and Research - Department of Operations Research

Now we witness the era of big data. That term "Big Data" means that data are difficult to be classified and manipulated easily due to its nature that variety of forms including text, voice, and video (Russom P., 2011). The immense storage medium, which have the ability to deal with a large variety of data and store them easily, have become a basic requirement to obtain clear information to make a decisions in the shortest possible period of time. Therefore, most the current traditional data analytic methods may not be suitable for processing streaming data with high feature dimensions because only a few methods have low time complexity, which is linear with both the number of samples and features. Furthermore, decision makers need to be able to gain valuable insights from such rapidity, varied and changing data, ranging from daily transactions to social network data and various other sources. The question that arises now is how to develop a high performance platform so as to efficiently analyze Big Data and how to design an appropriate mining algorithm to find the useful things from Big Data. In this methodology, used intelligent techniques for Big Data analysis by dividing the population data into the number of frames and taking a sample from each, and then collecting all the samples in one data for statistical analysis

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