The Naval Aviation Training Systems and Ranges program office (PMA-205) operates in data-rich information system environments, generating substantial volumes of data throughout its training and operational missions. These datasets can prove extremely useful in identifying performance trends of trainees or operational gaps in aviation assets. Due to sheer magnitude, sifting and analyzing of these data is a monumental task that only makes sense to be automated through computational methods, namely artificial intelligence/machine learning (AI/ML). This STTR topic reflects the PMA-205 need for a large dataset processing capability that ingests volumes of disparate aircrew training data streams to perform predictive analyses. With such a framework, Naval Aviation stakeholders, such as Chief of Naval Air Training (CNATRA) and Maritime Patrol Reconnaissance Force (MPRF), can be empowered with an intelligent AI/ML tool that can perform this data sifting and predictive analysis in an efficient and expedited manner. Soar Technology Inc. (SoarTech), with its academic partner Embry Riddle Aeronautical University (ERAU) and Naval aviation subject-matter expert (SME) team, Aviation Systems Engineering Company (ASEC), propose the Aggregated Data Evaluation & Prediction Tool (ADEPT). ADEPT is a data analyst-facing assistive software that processes aggregated, disparate domain-specific data streams from Navy data stores and enables predictive analysis and experimentation through ML modeling of these data, as directed by its SME human analyst user.
