12 May 2016 Foundations for context-aware information retrieval for proactive decision support
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Proceedings Volume 9851, Next-Generation Analyst IV; 985108 (2016); doi: 10.1117/12.2231152
Event: SPIE Defense + Security, 2016, Baltimore, Maryland, United States
Abstract
Intelligence analysts and military decision makers are faced with an onslaught of information. From the now ubiquitous presence of intelligence, surveillance, and reconnaissance (ISR) platforms providing large volumes of sensor data, to vast amounts of open source data in the form of news reports, blog postings, or social media postings, the amount of information available to a modern decision maker is staggering. Whether tasked with leading a military campaign or providing support for a humanitarian mission, being able to make sense of all the information available is a challenge. Due to the volume and velocity of this data, automated tools are required to help support reasoned, human decisions. In this paper we describe several automated techniques that are targeted at supporting decision making. Our approaches include modeling the kinematics of moving targets as motifs; developing normalcy models and detecting anomalies in kinematic data; automatically classifying the roles of users in social media; and modeling geo-spatial regions based on the behavior that takes place in them. These techniques cover a wide-range of potential decision maker needs.
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Ranjeev Mittu, Jessica Lin, Qingzhe Li, Yifeng Gao, Huzefa Rangwala, Peter Shargo, Joshua Robinson, Carolyn Rose, Paul Tunison, Matt Turek, Stephen Thomas, Phil Hanselman, "Foundations for context-aware information retrieval for proactive decision support", Proc. SPIE 9851, Next-Generation Analyst IV, 985108 (12 May 2016); doi: 10.1117/12.2231152; https://doi.org/10.1117/12.2231152
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KEYWORDS
Data modeling

Sensors

Web 2.0 technologies

Kinematics

Video

Denoising

Global Positioning System

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