Operator training using a weapon in a real-world environment is risky, expensive, time-consuming, and restricted to the given environment. In addition, governments are under intense scrutiny to provide security, yet they must also strive for efficiency and reduce spending. In other words, they must do more with less. Virtual simulation, is usually employed to solve these limitations. As the operator is trained to maximize weapon effectiveness, the effectiveness-focused training can be completed in an economical manner. Unfortunately, the training is completed in limited scenarios without objective levels of training factors for an individual operator to optimize the weapon effectiveness. Thus, the training will not be effective. For overcoming this problem, we suggest a methodology on guiding effectiveness-focused training of the weapon operator through usability assessments, big data, and Virtual and Constructive (VC) simulations. Usability assessments will be done to identify human factors affecting weapon’s effectiveness. Usability assessments consist of data-driven and judgment-driven analytics. The first is generated from raw simulation log files to evaluate the participant’s level of skill. The latter is generated from questionnaires, and evaluates the user’s background, experience, and opinions about various interactions within the simulation. The human factors must be found out based on several situations to reduce bias. VC simulations are executed under a variety of scenarios, and then, big data are generated such as structured, unstructured, semi-structured types. The big data, including completed questionnaires, are stored in a Hadoop Distributed File System (HDFS) and analyzed with processing via MapReduce, followed by an analytics tool. We can discover important human factors influencing weapon effectiveness, along with optimal values for these factors, which can be a guideline for effectiveness-focused training of the individual weapon operator.
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