15 May 2015 Generalist analysts at the edge and distributed analytics
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Abstract
Joint Vision 2020 highlights that achievement of ‘full spectrum dominance rests upon information superiority’ and that information capabilities are changing rapidly. Similarly the Eight Great Technologies and McKinsey Global Institute have highlighted the criticality of ‘Big Data’ technologies. But most ‘Big Data’ technologies are predicated on the availability of high quality/bandwidth distributed information Infrastructure and service rich systems, and much of the technology is designed for use by highly trained data scientists. In deployed military operations the context is radically different; many analysts are generalists as opposed to highly trained data scientists, and the information infrastructure is frequently significantly smaller, sparse and brittle but nevertheless complex. Further operations are highly dynamic, temporally challenging, and in an unfamiliar sociocultural environment. As Joint Vision 2020 states ‘the need to shape ambiguous situations at the low end of the range of operations will present special challenges’. This paper outlines the S&T challenges associated with adapting ‘Big Data’ technologies to build a distributed analytic capability for the deployed operations. In particular we will discuss issues associated with: a) The adoption of data analytic platforms and the need for adaption to a distributed coalition environment and tactical information infrastructures; b) The Volume, Velocity, Variety, Veracity, Viscosity and Value of information and information processing, storage and distribution capabilities; c) The nature of the situations to be understood and the resulting impact on abstract representations and synergistic human-machine teams; d) The role of the human in collaboratively extracting understanding from information and directing the information system.
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Gavin Pearson, Bhopinder Madahar, "Generalist analysts at the edge and distributed analytics", Proc. SPIE 9499, Next-Generation Analyst III, 94990O (15 May 2015); doi: 10.1117/12.2176563; https://doi.org/10.1117/12.2176563
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