Multi-Level Associative Content Environment (MACE) Navy SBIR FY2013.2 Sol No.: Navy SBIR FY2013.2 Topic No.: N132-131 Topic Title: Multi-Level Associative Content Environment (MACE) Proposal No.: N132-131-0755 Firm: Boston Fusion Corp. 1 Van de Graaff Drive Suite 107 Burlington, Massachusetts 01803-5176 Contact: Connie Fournelle Phone: (617) 583-5730 Abstract: Successful intelligence analysis requires analysts to wade through massive stores of uncertain data to associate concepts, individuals, locations, and resources. Current data systems are either designed to support massive data search and retrieval, or automated analysis, but lack the flexibility to do both well. What is needed is a system that can balance between these two, to maintain and flexibly navigate association data at multiple levels of detail, while avoiding information loss that can occur when either too much or too little data is persisted, presented, or analyzed. In response, we will develop the Multi-level Associated Content Environment (MACE), an association database management and analysis system implemented as a multi-level graph. In Phase I, we will build a data model and system design, and conduct a proof-of-concept demonstration to show that MACE will scale to petabytes of data in Phase II. MACE will incorporate associations between entities, documents, and concepts at multiple levels of detail, and will persist analytic tool inferences with connections to source data. Using graph databases, we will achieve analytic and run-time performance successes where traditional databases fail. MACE will leverage existing open software in a plug-and-play architecture to provide an open, license-free solution. Benefits: The Multi-level Associative Content Environment (MACE) program offers significant potential benefits to the US Government and potential commercial applications. It provides an efficient and scalable association database by using graph database technologies, supporting data persistence, analysis, and search; and leverages open source software to minimize ongoing expense in transition. The system design, multi-level data model, and prototype demonstration are the central artifacts of Phase I; together, they will show the potential of developing a scalable database architecture capable of enabling dramatically enhanced discovery of relationships within massive and potentially noisy data sets. Return