KEYWORDS: Intelligent sensors, Intelligence systems, Sensors, Data communications, Data processing, Image processing, Systems modeling, Analytical research, Complex systems, Video
Data collection processes supporting Intelligence, Surveillance, and Reconnaissance (ISR) missions have recently undergone a technological transition accomplished by investment in sensor platforms. Various agencies have made these investments to increase the resolution, duration, and quality of data collection, to provide more relevant and recent data to warfighters. However, while sensor improvements have increased the volume of high-resolution data, they often fail to improve situational awareness and actionable intelligence for the warfighter because it lacks efficient Processing, Exploitation, and Dissemination and filtering methods for mission-relevant information needs. The volume of collected ISR data often overwhelms manual and automated processes in modern analysis enterprises, resulting in underexploited data, insufficient, or lack of answers to information requests. The outcome is a significant breakdown in the analytical workflow. To cope with this data overload, many intelligence organizations have sought to re-organize their general staffing requirements and workflows to enhance team communication and coordination, with hopes of exploiting as much high-value data as possible and understanding the value of actionable intelligence well before its relevance has passed. Through this effort we have taken a scholarly approach to this problem by studying the evolution of Processing, Exploitation, and Dissemination, with a specific focus on the Army’s most recent evolutions using the Functional Resonance Analysis Method. This method investigates socio-technical processes by analyzing their intended functions and aspects to determine performance variabilities. Gaps are identified and recommendations about force structure and future R and D priorities to increase the throughput of the intelligence enterprise are discussed.
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.
KEYWORDS: Tactical intelligence, Web 2.0 technologies, Data modeling, Visual analytics, Visualization, Information technology, Databases, Prototyping, Standards development, Analytical research
Modern military environments place an increased emphasis on the collection and analysis of intelligence at the tactical
level. The deployment of analytical tools at the tactical level helps support the Warfighter’s need for rapid collection,
analysis, and dissemination of intelligence. However, given the lack of experience and staffing at the tactical level, most
of the available intelligence is not exploited.
Tactical environments are staffed by a new generation of intelligence analysts who are well-versed in modern
collaboration environments and social networking. An opportunity exists to enhance tactical intelligence analysis by
exploiting these personnel strengths, but is dependent on appropriately designed information sharing technologies.
Existing social information sharing technologies enable users to publish information quickly, but do not unite or
organize information in a manner that effectively supports intelligence analysis.
In this paper, we present an alternative approach to structuring and supporting tactical intelligence analysis that
combines the benefits of existing concepts, and provide detail on a prototype system embodying that approach. Since
this approach employs familiar collaboration support concepts from social media, it enables new-generation analysts to
identify the decision-relevant data scattered among databases and the mental models of other personnel, increasing the
timeliness of collaborative analysis. Also, the approach enables analysts to collaborate visually to associate
heterogeneous and uncertain data within the intelligence analysis process, increasing the robustness of collaborative
analyses.
Utilizing this familiar dynamic collaboration environment, we hope to achieve a significant reduction of time and skill
required to glean actionable intelligence in these challenging operational environments.
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