Cross Organizational Collaborative Discovery of Multi-Faceted Data Archives (CODIS)

The use of big data analytic tools is becoming necessary to unearth information from diverse datasets to satisfy needs across government agencies and commercial entities. Many operationally mature and commercially available tools already address challenges in extracting knowledge from raw data which ultimately informs decisions. A lingering challenge is that many data archives remain isolated (for classification, location or legacy reasons) creating data silos, making it difficult or impossible to discover knowledge across silos. The objective of this work is to develop intuitive knowledge exploration tools to navigate and visualize large-scale multi-faceted datasets for the purpose of discovering patterns and connections between and within datasets while ensuring that the raw data remains segregated. We seek to support better and faster decisions through unguided knowledge discovery and inquire driven knowledge discovery. Following the success of our phase 1 prototype we will utilize scalable search technology, apply real time document processing and enrichment pipelines, support distributed graph computing for large scale graph analytics and enable traversal across large graphs of derived entities.