Elizabeth Bowman, Matt Turek, Paul Tunison, Reed Porter, Steve Thomas, Vadas Gintautas, Peter Shargo, Jessica Lin, Qingzhe Li, Yifeng Gao, Xiaosheng Li, Ranjeev Mittu, Carolyn Penstein Rosé, Keith Maki, Chris Bogart, Samrihdi Shree Choudhari
KEYWORDS: Data modeling, Web 2.0 technologies, Sensors, Kinematics, Data mining, Analytical research, Video processing, Video, Analytics, Machine learning
Today’s warfighters operate in a highly dynamic and uncertain world, and face many competing demands. Asymmetric warfare and the new focus on small, agile forces has altered the framework by which time critical information is digested and acted upon by decision makers. Finding and integrating decision-relevant information is increasingly difficult in data-dense environments. In this new information environment, agile data algorithms, machine learning software, and threat alert mechanisms must be developed to automatically create alerts and drive quick response. Yet these advanced technologies must be balanced with awareness of the underlying context to accurately interpret machine-processed indicators and warnings and recommendations. One promising approach to this challenge brings together information retrieval strategies from text, video, and imagery. In this paper, we describe a technology demonstration that represents two years of tri-service research seeking to meld text and video for enhanced content awareness. The demonstration used multisource data to find an intelligence solution to a problem using a common dataset. Three technology highlights from this effort include 1) Incorporation of external sources of context into imagery normalcy modeling and anomaly detection capabilities, 2) Automated discovery and monitoring of targeted users from social media text, regardless of language, and 3) The concurrent use of text and imagery to characterize behaviour using the concept of kinematic and text motifs to detect novel and anomalous patterns. Our demonstration provided a technology baseline for exploiting heterogeneous data sources to deliver timely and accurate synopses of data that contribute to a dynamic and comprehensive worldview.
Ranjeev Mittu, Jessica Lin, Qingzhe Li, Yifeng Gao, Huzefa Rangwala, Peter Shargo, Joshua Robinson, Carolyn Rose, Paul Tunison, Matt Turek, Stephen Thomas, Phil Hanselman
KEYWORDS: Web 2.0 technologies, Data modeling, Sensors, Kinematics, Video, Global Positioning System, Denoising, Statistical modeling, Systems modeling, Phase modulation
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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