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Terrorist Activities worldwide has led to the development of sophisticated methodologies for analyzing terrorist groups and networks. Ongoing and past research has found that Social Network Analysis (SNA) is most effective method for predictive counter-terrorism. Social Network Analysis (SNA) is an approach towards analyzing the terrorist networks to better understand the underlying structure of a network and to detect key players within the network and their links throughout the network. It is also need of the hour to convert available raw data into valuable information for the purpose of global security. Comparative study among SNA tools testify their applicability and usefulness for data gathered through online and offline social sources. However it is advised to incorporate temporal analysis using data mining methods, to improve the capability of SNA tools to handle dynamic social media data. This paper examine various aspects of Social Network Analysis as applied to terrorism, taking empirical data, and open source data based studies into account. This work primarily focuses on different types of decentralized terrorist networks and nodes. The nodes can be classified as organizations, places or persons. We take help of varied centrality measures to identify key players in this network.

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This paper, describes Concept of Big Data which is collection of large data set that cannot be proceed by traditional computational techniques. Therefore Hadoop technology designed to process Big Data. Hadoop is the platform in businesses for Big Data processing. Hadoop is an open source, Java-based programming framework which supports the processing and storage of extremely large data sets in a distributed computing environment. It helps Big Data analytics by overcoming the difficulties that are usually faced in handling Big Data. Hadoop can break down large computational problems into smaller tasks as smaller elements can be analyzed economically and quickly [1].Hadoop is an open-source software framework for storing data and running applications on clusters of commodity hardware. It provides massive storage for various kind of data, enormous processing power and the ability to handle virtually limitless concurrent tasks. All these parts are analyzed in parallel and the results of the analysis are regrouped to produce the final output.