Navy Taps IAI to Develop a Scalable Event Extractor for Multi-Level Event Data and Pattern Archiving

Naval Intelligence Surveillance and Reconnaissance systems collect large amounts of data daily. There is a critical need to capture important information from this data before it is lost, and to preserve it in a manner that enables efficient data retrieval for future analysis. To address this, IAI and collaborators from the University of Illinois Urbana-Champaign and Echo Analytics Group, will develop SEE, a Scalable Event Extractor for Multi-Level Event Data and Pattern Archiving. SEE integrates a data collection and integration module that prepares data for further analysis, with an information extraction module that extracts entities, relationships, and events from the large-scale raw data. It also includes a multi-level event data and pattern archiving architecture for efficient information recall and analytics, and a user interface to deliver the query and analytics results to analysts. IAI’s big data technologies will be the foundation of all the SEE algorithms and architectures. The key innovation of SEE is the multi-level event data and pattern archiving architecture, which allows fast information retrieval, efficient information storage, easy to roll-up and drill-down information, and scalable event data and pattern archiving. SEE can be directly applied to military and intelligence contexts. It has significant commercial potential for extracting and archiving events from publications for publication data reviewing, from news in the sports or finance field, and from Electronic Medical Records (EMR). Internet search engines would also benefit from this technology, and information retrieval based on second- or higher-order association would transform content delivery.