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Proceedings Paper

A visual analytic framework for data fusion in investigative intelligence
Author(s): Guoray Cai; Geoff Gross; James Llinas; David Hall
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Paper Abstract

Intelligence analysis depends on data fusion systems to provide capabilities of detecting and tracking important objects, events, and their relationships in connection to an analytical situation. However, automated data fusion technologies are not mature enough to offer reliable and trustworthy information for situation awareness. Given the trend of increasing sophistication of data fusion algorithms and loss of transparency in data fusion process, analysts are left out of the data fusion process cycle with little to no control and confidence on the data fusion outcome. Following the recent rethinking of data fusion as human-centered process, this paper proposes a conceptual framework towards developing alternative data fusion architecture. This idea is inspired by the recent advances in our understanding of human cognitive systems, the science of visual analytics, and the latest thinking about human-centered data fusion. Our conceptual framework is supported by an analysis of the limitation of existing fully automated data fusion systems where the effectiveness of important algorithmic decisions depend on the availability of expert knowledge or the knowledge of the analyst’s mental state in an investigation. The success of this effort will result in next generation data fusion systems that can be better trusted while maintaining high throughput.

Paper Details

Date Published: 22 May 2014
PDF: 9 pages
Proc. SPIE 9122, Next-Generation Analyst II, 91220A (22 May 2014); doi: 10.1117/12.2053161
Show Author Affiliations
Guoray Cai, The Pennsylvania State Univ. (United States)
Geoff Gross, Univ. at Buffalo (United States)
James Llinas, Univ. at Buffalo (United States)
David Hall, The Pennsylvania State Univ. (United States)


Published in SPIE Proceedings Vol. 9122:
Next-Generation Analyst II
Barbara D. Broome; David L. Hall; James Llinas, Editor(s)

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