Modern decision support systems are required to integrate and synthesize a rapidly expanding collection of real-time data feeds: sensor, analysts’ reports, social media, chat, documents, manifests, logistical data, and system logs (to name just a few). Custom databases for sensors, graphs, documents, and transactions (just to name a few) provide 100x better performance than general purpose databases. The performance benefits of custom databases have resulted in proliferation of data specific databases and most modern decision support systems contain five or more distinct data storage systems. This diversity of storage has resulted in paradigm shift to the “polystore” database era. In this presentation, I will discuss polystore database, our reference implementation, BigDAWG, and its application to medical and scientific problems.
LAS aims to bring together a multi-disciplinary group of academic, industry, and government researchers, analysts and managers together to re-engineer the intelligence analysis process to promote predictive analysis. LAS will do this by conducting both classified and unclassified research in a variety of areas of research. The research done in this area will serve as the foundation for mission effects and integrated back into the enterprise.
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