الفهرس 
Chapter (1) IntroductionOnly 14 pages are availabe for public view
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Abstract
We live in a world flooded with data. Some even see it as the fuel that drives all companies to reach their goals. Business Intelligence is introduced to enable a company to get the power of its data to be able for the competition in the rougher market. Because business needs to make decisions in a fast and reliable manner, analysis the big data in real time become interested issue. Although the significant efforts that done in this area, big data analysis in real time is still need additional effort to enhance the performance and reduce the required time. This thesis introduces a framework to analyze big data in real-time using the K-means clustering technique. Although the K-means is widely used in clustering, its processing requirement can be a problem in big data and real-time systems. In this research, the K-means algorithm is adapted to be suitable for the case of big data and real-time systems. The proposed framework introduces two models the first one uses historical data to create a model which deployed to real-time data and the second one analyzes the data in real-time without historical data. Experimental results show that the accuracy of the proposed framework with its two models is approximately 0.5, 0.34 respectively using the Silhouette Coefficient measurement.