SBIR-STTR Award

To address the DHS need for a new data analytics engine to correlate social media comments and activity with incident command data, Physical Optics Corporation (POC) proposes to develop a new Real-time Information Contextual Correlation and Analysis (RICCA) software system based on Bayesian analytics, multiresolution event context hypercube (MECH) representation, and multi-source data analysis and fusion using social media ontologies and emergency management ontologies. RICCA offers automatic and real-time extraction of multiple external factors relevant to an event of interest from social media outlets, to improve incident command's situational awareness and understanding. In Phase I, POC will demonstrate the feasibility of RICCA by developing a set of operational scenarios, identifying the external factors in social media and operational incident data, developing core analytics modules, and implementing algorithms to measure performance and improvements. In Phase II, POC plans to mature the RICCA prototype and correlation and analysis algorithms to support a pilot protocol by which a social media feed is correlated with operational incident data. The successful completion of this project at the end of Phase III will benefit the nation in both government and commercial sectors by improving the situational awareness and decision-making capabilities of incident commands and significantly improving the effectiveness of response decisions and actions. Commercial applications for this technology range from personal use to business intelligence, news gathering, trend analysis, and data gathering applications.

To address the DHS need for a new data analytics engine to correlate social media comments and activity with incident command data, Physical Optics Corporation (POC) proposes, in Phase II, to advance a new Real-time Information Contextual Correlation and Analysis (RICCA) software system proven feasible in Phase I. RICCA is based on unstructured data analysis and integration and event context modeling. Its advanced contextual analytics engine enables automated processing flow to retrieve social media data from multiple outlets (Facebook, Twitter, YouTube), extract environmental, social, meteorological, political, economic, and other factors relevant to an event of interest, correlate them in geo-space and time with data stored in a computer-aided-dispatch (CAD) system, and generate alerts for first responders and emergency/incident/crisis management. The innovation in unstructured data processing and integration and multi-resolution event context modeling can improve incident command's situational awareness and understanding. In Phase I, POC demonstrated the feasibility of RICCA by developing a set of operational scenarios, identifying the external factors in social media and operational incident data, developing core analytics modules, and implementing algorithms to measure performance and improvements. In Phase II, POC plans to mature the RICCA prototype and its correlation and analysis algorithms for the target scenario established in Phase I and support a pilot protocol by which a social media feed is correlated with operational incident data. Validation and trust algorithms will also be developed to support more timely and targeted response actions and allow for escalation preparedness.