In the rapidly changing and expanding environments in which modern business occurs, the need for data analytics has never been greater. As the cost of data-warehousing continues to plummet, many corporations are shifting away from the antiquated concept of "useful data" to that of the collection of seemingly disparate data. This increase in data presents a new problem: analytics. Leveraging technologies such as Hadoop, Hbase, and HyperTable, we have created an Intelligent Agent framework that allows big-data analytics to be conducted in a manner never before experienced.
Our framework deviates from the standard use of the MapReduce paradigm, commonly used by eliminating a single static or heuristic algorithm, instead using a network of Intelligent Agents (IA's). When used for analytics, each agent in an Intelligent Agent Framework performs its uniquely programmed task while communicating its results to other agents, resulting in a dynamically collaborative IA system. Within this framework, each agent's action directly affects the action of one or more other agents; resulting in the IA framework correlating seemingly unrelated data to find logic in the chaos of the big-data. The MapReduce job can then be run to perform further data reduction required to provide meaningful answers for the users.