Using Big Data Analytics to Create a Predictive Model for Joint Strike Fighter
The amount of information needed to acquire knowledge on today‚??s acquisition systems is growing exponentially due to more complex, higher resolution, software-intensive acquisition systems that need to operate in System-of-Systems (SoS), Family-of-Systems (FoS), Joint, and Coalition environments. Unfortunately, the tools and methods necessary to rapidly collect, aggregate, and analyze this information have not evolved as a whole in conjunction with this increased system complexity and, therefore, has made analysis and evaluation increasingly deficient and ineffective. The Test Resource Management Center‚??s (TRMC‚??s) vision is to build a DoD test and evaluation (T&E) knowledge management (KM) and analysis capability that leverages commercial big data analysis and cloud computing technologies to improve evaluation quality and reduce decision-making time. An evaluation revolution, starting with the Joint Strike Fighter (JSF) program, is underway to ensure the T&E community can support the demands of next-generation weapon systems. The true product of T&E is knowledge ascertained through the collection of information about a system or item under test. However, the T&E community‚??s ability to provide this knowledge is hampered by more complex systems, more complex environments, and the need to be more agile in support of strategic initiatives, such as agile acquisition and the 3rd Offset Strategy. This increased complexity and need for speed cause delayed analysis and problems that go undetected during T&E. The primary reason for these shortfalls is antiquated tools and processes that make data hard to locate, aggregate, and convert into knowledge. In short, DoD has not evolved its evaluation infrastructure as its weapon systems have evolved.
Big Data, Data Analytics, Knowledge Management, Data Management, Virtualization, Cloud Computing, Predictive Maintainance, Department of Defense, Test and Evaluation