SBIR/STTR Award attributes
Big data analysis is a critical component of product and process evaluation in the commercial sphere. The explosion of online advertising, social networks, and streaming media has led to the need for analysis techniques suited to generating insights from extremely large quantities of data. The object of this research project is to adapt data mining techniques, including machine learning, which have been successfully used commercially to manage and analyze user data and apply them to BMDS science and engineering simulation data. The types of data to be considered will include sensor updates, radar cross section data, and actual/perceived truth data. The opportunity exists to leverage IDT’s existing data management tool suite and BMDS expertise, and create a Data Management, Mining, and Analysis Toolkit (DMMAT) that will detect, classify, and communicate patterns found in data recorded from BMDS simulation runs. In Phase I, the DMMAT data analysis pipeline will be created and loaded with unclassified representative BMDS simulation data. Research, development, and implementation of cutting edge data science techniques will be performed to determine those most applicable to the representative and real world data. These algorithm and techniques will be integrated into the DMMAT framework. Approved for Public Release | 18-MDA-9817 (23 Oct 18)
