INVISQUE as a Tool for Intelligence Analysis: The Construction of Explanatory Narratives

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.

[1]  Analyst's Workspace: An embodied sensemaking environment for large, high-resolution displays , 2012, 2012 IEEE Conference on Visual Analytics Science and Technology (VAST).

[2]  Jennifer K. Phillips,et al.  A Data–Frame Theory of Sensemaking , 2007 .

[3]  John T. Stasko,et al.  Evaluating visual analytics systems for investigative analysis: Deriving design principles from a case study , 2009, 2009 IEEE Symposium on Visual Analytics Science and Technology.

[4]  Malcolm Slaney,et al.  Being Literate with Large Document Collections: Observational Studies and Cost Structure Tradeoffs , 2006, Proceedings of the 39th Annual Hawaii International Conference on System Sciences (HICSS'06).

[5]  Christopher Andrews,et al.  Space to think: large high-resolution displays for sensemaking , 2010, CHI.

[6]  Ann Blandford,et al.  Making Sense of Digital Footprints in Team-Based Legal Investigations: The Acquisition of Focus , 2011, Hum. Comput. Interact..

[7]  Ben Shneiderman,et al.  Visual information seeking: tight coupling of dynamic query filters with starfield displays , 1994, CHI '94.

[8]  Dorrit Billman,et al.  Medical sensemaking with entity workspace , 2007, CHI.

[9]  Frank M. Shipman,et al.  Spatial hypertext and the practice of information triage , 1997, HYPERTEXT '97.

[10]  H. Sacks,et al.  Readings in Medical Artificial Intelligence: The First Decade , 1985 .

[11]  Stuart K. Card,et al.  The cost structure of sensemaking , 1993, INTERCHI.

[12]  Frances J. Milliken,et al.  Executives’ Perceptual Filters: What They Notice and How They Make Sense , 1988 .

[13]  Natasa Milic-Frayling,et al.  Materializing the query with facet-streams: a hybrid surface for collaborative search on tabletops , 2011, CHI.

[14]  Andruid Kerne,et al.  An Experimental Method for Measuring the Emergence of New Ideas in Information Discovery , 2008, Int. J. Hum. Comput. Interact..

[15]  Neesha Kodagoda,et al.  INVISQUE: intuitive information exploration through interactive visualization , 2011, CHI EA '11.

[16]  James D. Hollan,et al.  Spatial Tools for Managing Personal Information Collections , 2005, Proceedings of the 38th Annual Hawaii International Conference on System Sciences.

[17]  James J. Thomas,et al.  Visualizing the non-visual: spatial analysis and interaction with information from text documents , 1995, Proceedings of Visualization 1995 Conference.

[18]  Chris North,et al.  Semantic Interaction for Sensemaking: Inferring Analytical Reasoning for Model Steering , 2012, IEEE Transactions on Visualization and Computer Graphics.

[19]  Michelle L. Gregory,et al.  Visual Analysis of Weblog Content , 2007, ICWSM.

[20]  P. Pirolli,et al.  The Sensemaking Process and Leverage Points for Analyst Technology as Identified Through Cognitive Task Analysis , 2007 .

[21]  Nawaz Khan,et al.  Interactive visualization for low literacy users: from lessons learnt to design , 2012, CHI.

[22]  Yan Liu,et al.  Integrated Faceted Browser and Direct Search to Enhance Information Retrieval in Text-Based Digital Libraries , 2011, Int. J. Hum. Comput. Interact..

[23]  B. L. William Wong,et al.  Electronic resource discovery systems: from user behaviour to design , 2010, NordiCHI.

[24]  Daniel A. Keim,et al.  Document Cards: A Top Trumps Visualization for Documents , 2009, IEEE Transactions on Visualization and Computer Graphics.

[25]  Paul J. Feltovich,et al.  LCS: The role and development of medivcal knowledge in diagnostic expertise , 1984 .

[26]  Alexander W. Skaburskis,et al.  The Sandbox for analysis: concepts and methods , 2006, CHI.

[27]  Richards J. Heuer,et al.  Psychology of Intelligence Analysis , 1999 .

[28]  John T. Stasko,et al.  How Can Visual Analytics Assist Investigative Analysis? Design Implications from an Evaluation , 2011, IEEE Transactions on Visualization and Computer Graphics.

[29]  Chris North,et al.  Semantic interaction for visual text analytics , 2012, CHI.

[30]  Celestine A. Ntuen,et al.  Designing an Information Visualization Tool for Sensemaking , 2010, Int. J. Hum. Comput. Interact..

[31]  Frank M. Shipman,et al.  The visual knowledge builder: a second generation spatial hypertext , 2001, HYPERTEXT '01.

[32]  M. Weggeman.  Sensemaking in organisations , 1996 .

[33]  Marti A. Hearst.  Search User Interfaces , 2009 .

[34]  Scott P. Robertson,et al.  Proceedings of the SIGCHI Conference on Human Factors in Computing Systems , 1991 .

[35]  Inessa Seifert.  A Pool of Queries: Interactive Multidimensional Query Visualization for Information Seeking in Digital Libraries , 2011, Inf. Vis..