Analytic provenance as constructs of behavioural markers for externalizing thinking processes in criminal intelligence analysis

Islam, Junayed, Wong, B. L. William ORCID logoORCID: and Xu, Kai ORCID logoORCID: (2018) Analytic provenance as constructs of behavioural markers for externalizing thinking processes in criminal intelligence analysis. In: Community-Oriented Policing and Technological Innovations. Leventakis, Georgios and Haberfeld, M. R., eds. SpringerBriefs in Criminology . Springer, pp. 95-105. ISBN 9783319892931, e-ISBN 9783319892948. [Book Section] (doi:10.1007/978-3-319-89294-8_10)

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Studying how analysts use interaction in visualization systems is an important part of evaluating how well these interactions support analysis needs, like generating insights or performing tasks. Analytic Provenance commonly known as interaction histories contains information about the sequence of choices that analysts make when exploring data or performing a task. This research work presents a compositional reductionist approach as a way of externalizing analyst’s thinking processes by using markers of analytical behaviour extracted from such interaction histories. Set of Behavioural Markers (BMs) have been identified through a workshop with domain experts and a systematic literature review to use them as cognitive attributes of imagination, insight, transparency, fluidity and rigour to enhance performance in criminal intelligence analysis. A low level semantic action sequence computation also has been proposed as a detection approach of identified BMs and found from computation that BMs can act as bridge between human cognition and computation through semantic interaction. This research work has addressed problems of existing qualitative experiments to extract these BMs through cognitive task analysis and found that the proposed computational technique can be a supplementary approach for validating experimental results.

Item Type: Book Section
Additional Information: Series Print ISSN: 2192-8533. Series Online ISSN: 2192-8541
Research Areas: A. > School of Science and Technology > Computer Science
Item ID: 24191
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Depositing User: Junayed Islam
Date Deposited: 30 Apr 2018 10:21
Last Modified: 29 Nov 2022 19:55

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