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<p>Navy Manpower, Personnel, Training, and Education (MPTE) decision makers require improved access to the information obtained from the vast amounts of data contained in a number of disparate databases/data stores in order to make informed decisions and understand second- and third-order effects of those decisions. Toward this end, the research effort of this thesis was two-fold. First, this thesis examined and proposed an end-to-end application architecture for performing analytics for Navy. Second, it developed a decision tree model to predict retention of post-command aviators, using the Cross-Industry Standard Process for Data Mining (CRISP-DM), in support of one Navy MPTE’s concerns: retention in post-command aviator community. This research concluded that with the exponential collection and growth of diverse data, there is a need for a combination of Big Data and traditional data warehousing architectures to support analytics at MPTE. The data-mining effort developed a preliminary predictive model for post-command aviation retention and concluded that the number of NOBCs, particularly non-aviation NOBCs, was the most important indicator for predicting retention. Additional data sources particularly those that contain Fitness Reports/Evaluations need to be included in order to improve the accuracy of the model.
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<br>Subjects: Big Data; application architecture; enterprise architecture; OPNAV N1; manpower; personnel; training; education; predictive modeling; CRISP-DM; aviation community retention</div>
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<dl><dd>Caindoy, Khristian C.</dd>
<dd>Moazzami, Armin</dd>
<dd>Santos, Anthony M.</dd></dl></div>
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