In the recent years, DoD has been acquiring major C4ISR systems to migrate towards network centric warfare. By DoD 5000 series policies all C4ISR systems go through formal test & evaluation process to ensure all level of requirements are met. Based on requirements analysis evaluators layout Measure of Performance (MoP) and Measure of Effectiveness (MoE) matrix. Evaluators also builds data source matrix for measurable evaluation focus areas. Due to network centric nature of all C4ISR systems most measurement depend on IP packet data analysis.
Such highly complex data grows enormously with fast networks and sophisticated sensor devices. Big Data Analytics refers to the collection, storage, processing, analysis and visualization of massive volume of complex data set. Traditionally, such data has been handled using SQL based relational databases which runs on normal desktops and servers. However, with new technology introduced by Google MapReduce simplified data processing on large clusters, such implementation is a total game changer for big data analysis. One open source implementation of MapReduce is Hadoop software framework which uses HDFS (Hadoop Distributed File System) and MapReduce to analyze big data on clusters of inexpensive hardware. Such software framework implementation is in a distributed computing environment. The integrated software environment provided by the Hadoop project is optimized for efficient processing of big data in a reliable manner. With the advent of network centric C4ISR systems, the DoD Test & Evaluation community has seen a massive growth in data that is generated with every test cycles. With recent innovation made available by MapReduce and Hadoop software framework, there has been a major breakthrough in data management and analysis. There are many No SQL based database techniques for organizing big data are available. Given these various possibilities it would help systems evaluators to go from data to decisions in matter of hours.
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