Measuring knowledge: investigative research into the quantification of performance within a contextual multi-source PED fusion process

Larry A. Scarff; Dustin Burke; Eric Jones; Lynne Gilfillan; Stephanie Pratt; Cullen Jackson; Shawn Weil; Stephen Fiore

doi:10.1117/12.2015303

29 May 2013 Measuring knowledge: investigative research into the quantification of performance within a contextual multi-source PED fusion process

Larry A. Scarff, Dustin Burke, Eric Jones, Lynne Gilfillan, Stephanie Pratt, Cullen Jackson, Shawn Weil, Stephen Fiore

Author Affiliations +

Proceedings Volume 8756, Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2013; 87560R (2013) https://doi.org/10.1117/12.2015303
Event: SPIE Defense, Security, and Sensing, 2013, Baltimore, Maryland, United States

Abstract

Most intelligence analysts currently use Information Products (IP) from multiple sources with very different characteristics to perform a variety of intelligence tasks. In order to maximize the analysts’ efficacy (and ultimately provide intelligent automation), it is important to understand how and what each IP within the set of IPs contributes to the accuracy and validity of the analytic result. This paper describes initial research toward the development of a scale, analogous to the National Imagery Interpretability Scale (NIIRS), which will measure the knowledge contribution of each of the multi-source IPs, as well as measuring the extent to which the IP set as a whole meets the enduser’s intelligence need – which is actionable knowledge. This scale, the Knowledge-NIIRS (KnIIRS), when completed, will support the measurement of the quality and quantity of information gained through multi-source IP fusion and enables the development of smart (automated) tools for analysts using the next generation of PED workstations. The results of this initial study indicate that analysts are capable of making judgments that reflect the “value” of fused information, and that the judgments they make vary along at least two dimensions. Furthermore, there are substantial and significant differences among analysts in how they make these judgments that must be considered for further scale development. We suggest that the KnIIRS objectives and its derived understandings offer important and critical insights to enable automation that will achieve the goal to deliver actionable knowledge.

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Larry A. Scarff, Dustin Burke, Eric Jones, Lynne Gilfillan, Stephanie Pratt, Cullen Jackson, Shawn Weil, and Stephen Fiore "Measuring knowledge: investigative research into the quantification of performance within a contextual multi-source PED fusion process", Proc. SPIE 8756, Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2013, 87560R (29 May 2013); https://doi.org/10.1117/12.2015303

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Statistical analysis

Analytical research

Error analysis

LIDAR

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Information fusion

Intelligence systems

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