Multi-Level Associative Content Environment (MACE)

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.