Advanced text and video analytics for proactive decision making
| Autor: | Yifeng Gao, Steve Thomas, Elizabeth K. Bowman, Vadas Gintautas, Matthew William Turek, Carolyn Penstein Rosé, Ranjeev Mittu, Reed Porter, Jessica Lin, Christopher Bogart, Qingzhe Li, Xiaosheng Li, Peter Shargo, Samrihdi Shree Choudhari, Paul Tunison, Keith Maki |
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| Rok vydání: | 2017 |
| Předmět: |
Web analytics
Decision engineering business.industry Computer science Context (language use) 02 engineering and technology 021001 nanoscience & nanotechnology 01 natural sciences Data science 010309 optics Software analytics Software Business analytics Analytics 0103 physical sciences Semantic analytics Social media 0210 nano-technology business Agile software development |
| Zdroj: | SPIE Proceedings. |
| ISSN: | 0277-786X |
| DOI: | 10.1117/12.2276369 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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