Resolving Independent Perspectives by Providing Learning-Enabled Enhanced Fusion For Elastic Cloud Technologies (RIPPLE-EFFECT)

To maintain situational awareness, analysts must sift through and fuse information across multiple documents, data sources, and modalities (text, imagery, and biometrics). The emergence of Big Data has placed an enormous burden on the analyst as the volume of data to examine has increased dramatically while the analyst"s capacity to understand and fuse information remains constant. Additionally, the data remains distributed across geographically separated systems with low-bandwidth connections. The analyst is presented with an incomplete data space from which to produce an intelligence picture. DAC proposes a system called Resolving Independent Perspectives by Providing Learning Enabled Enhance Fusion For Elastic Cloud Technologies (RIPPLE-EFFECT). RIPPLE-EFFECT provides a framework to fuse semantic enhancements from multiple vendors through the use of machine learning algorithms which infer correlations between both the semantic structure and the extracted semantic content. RIPPLE-EFFECT supports scalable cross-document inference over the semantically enhanced data space with dynamically evolving search patterns based on data encountered during the search and the semantic meaning behind the initial query. RIPPLE-EFFECT maintains a consistent data space and intelligence picture across geographically separated systems specialized to the area of interest and time period of interest for each system through lock-free continuous synchronization.