Transition of Mission Planning Software to a Next Generation Component Based, Open Architecture using Advanced Refactoring Technology

Timely and accurate dissemination of mission critical data is vital to ensure optimal execution of military operations in the battlespace. A significant number of critical systems such as the Joint Mission Planning Systems (JMPS) are wired tightly to non-centralized, individual platforms found in system of system networks. These planning systems are costly to maintain, and the ability to quickly evolve and scale software to support future mission planning is called into question. The need exists to augment and leverage existing technology capabilities embedded in software systems so that it can be dynamically repurposed to support multiplatform mission development, including mission planning for manned and unmanned aircraft. This proposal seeks to apply Big-Data techniques, especially Machine Learning coupled with Feature Identification, to the problem of rapidly obtaining critical mission planning information from disparate systems by utilizing machine learning to dynamically harvest data access algorithms and rapidly refactor them for use in mission planning.