The broader impact/commercial potential of this Phase I project is to process extremely large data sets efficiently. The sheer abundance of aerospace data is simply too overwhelming for an individual or team to have command of and control from memory and experience. This project will develop a system that can provide additional support through machine learning (ML) applied to big data sets where different types of data are integrated. It can aid civilian and military readiness, access, and speed during all phases of system development, as it reduces lifecycle cost and improves information access during production, test, verification, operations, and support. This capability can be applied to aerospace and non-aerospace test environments, long spaceflight missions, and other contexts where information in a large document set requires instant accessibility and provides optimal actionable intelligence. This Small Business Innovation Research (SBIR) Phase I project demonstrates the application of agent-based cognitive assistance in a real-world space operations environment, integrating machine learning (ML) and Natural Language Processing (NLP) analytical tools. To improve the speed and lower the cost of human decision making, the latest state-of-the-art developments in search, indexing, computational linguistics, and broadly speaking, machine learning and natural language processing, can be leveraged to simplify and improve the accessibility of a huge corpus of highly technical documentation. This project will demonstrate that all types of large datasets, including text in a multitude of formats, numerical, geospatial, structured and unstructured, can be interrogated using an advanced distributed open-source search engine. The information value of the search engine results is enhanced by analyzing metadata produced from NLP ML algorithms.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
