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Proceedings Paper

Employing socially driven techniques for framing, contextualization, and collaboration in complex analytical threads
Author(s): Arthur Wollocko; Jennifer Danczyk; Michael Farry; Michael Jenkins; Martin Voshell
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Paper Abstract

The proliferation of sensor technologies continues to impact Intelligence Analysis (IA) work domains. Historical procurement focus on sensor platform development and acquisition has resulted in increasingly advanced collection systems; however, such systems often demonstrate classic data overload conditions by placing increased burdens on already overtaxed human operators and analysts. Support technologies and improved interfaces have begun to emerge to ease that burden, but these often focus on single modalities or sensor platforms rather than underlying operator and analyst support needs, resulting in systems that do not adequately leverage their natural human attentional competencies, unique skills, and training. One particular reason why emerging support tools often fail is due to the gap between military applications and their functions, and the functions and capabilities afforded by cutting edge technology employed daily by modern knowledge workers who are increasingly “digitally native.” With the entry of Generation Y into these workplaces, “net generation” analysts, who are familiar with socially driven platforms that excel at giving users insight into large data sets while keeping cognitive burdens at a minimum, are creating opportunities for enhanced workflows. By using these ubiquitous platforms, net generation analysts have trained skills in discovering new information socially, tracking trends among affinity groups, and disseminating information. However, these functions are currently under-supported by existing tools. In this paper, we describe how socially driven techniques can be contextualized to frame complex analytical threads throughout the IA process. This paper focuses specifically on collaborative support technology development efforts for a team of operators and analysts. Our work focuses on under-supported functions in current working environments, and identifies opportunities to improve a team’s ability to discover new information and disseminate insightful analytic findings. We describe our Cognitive Systems Engineering approach to developing a novel collaborative enterprise IA system that combines modern collaboration tools with familiar contemporary social technologies. Our current findings detail specific cognitive and collaborative work support functions that defined the design requirements for a prototype analyst collaborative support environment.

Paper Details

Date Published: 15 May 2015
PDF: 12 pages
Proc. SPIE 9499, Next-Generation Analyst III, 949905 (15 May 2015); doi: 10.1117/12.2177047
Show Author Affiliations
Arthur Wollocko, Charles River Analytics, Inc. (United States)
Jennifer Danczyk, Charles River Analytics, Inc. (United States)
Michael Farry, Charles River Analytics, Inc. (United States)
Michael Jenkins, Charles River Analytics, Inc. (United States)
Martin Voshell, Charles River Analytics, Inc. (United States)

Published in SPIE Proceedings Vol. 9499:
Next-Generation Analyst III
Barbara D. Broome; Timothy P. Hanratty; David L. Hall; James Llinas, Editor(s)

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