Scoring Analysis via Data Integration and Information Extraction (SADIIE)

Applied Technical Systems, Inc. (ATS) proposes to leverage its expertise with maintenance and logistics support of Naval systems and its experience in developing data-rich software solutions to research, design, and develop a data integration and analysis system that is capable of fusing unstructured (textual) and structured (machine-readable) data sources to provide a quantitative assessment (score) of the effectiveness of Naval maintenance and sustainment processes. Assessing whether existing systems and procedures are meeting their goals requires bringing together data from across multiple, disconnected silos. Much of the vital data that identifies key performance indicators, thresholds for acceptable quality or cost, or desirable outcomes are only available in unstructured text documents. We propose a system that allows users with no prior experience in Natural Language Processing (NLP) or Information Extraction (IE) practices to interact with an automated system that learns over time how to extract the data they require. This data, in turn, feeds a scoring capability that allows users to construct and monitor quantitative measures of the effectiveness of existing or planned sustainment practices. Such data can then been used to provide meaningful, objective information to support process improvement initiatives.