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INVISQUE as a Tool for Intelligence Analysis: The Construction of Explanatory Narratives
Author(s) -
Chris Rooney,
Simon Attfield,
B. L. William Wong,
Sharmin Choudhury
Publication year - 2014
Publication title -
international journal of human-computer interaction
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.687
H-Index - 67
eISSN - 1532-7590
pISSN - 1044-7318
DOI - 10.1080/10447318.2014.905422
Subject(s) - sensemaking , narrative , set (abstract data type) , computer science , metaphor , interface (matter) , intelligence analysis , data science , cluster analysis , human–computer interaction , artificial intelligence , philosophy , linguistics , computer security , bubble , maximum bubble pressure method , parallel computing , programming language
This article reports an exploratory user study in which a group of civil servants with experience of, or involvement in, intelligence analysis used the tool INVISQUE to address a problem using the 2011 VAST data set. INVISQUE uses a visual metaphor that combines searching, clustering, and sorting of document surrogates with free-form manipulation on an infinite canvas. The study looks into exposing the behaviors and related cognitive strategies that users would employ to better understand how this and similar environments might better support intelligence type work. The results include the observation that the search and spatial features of the system supported participants in establishing, elaborating, and systematically evaluating explanatory narratives that accounted for the data. Also, visual persistence at the interface allowed them to keep track of searches and to re-find documents when their importance became apparent. The article concludes with reflections on our findings and propose a set of guidelines for developing systems that support sensemaking.

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