The Scalable Reasoning System: Lightweight visualization for distributed analytics

A central challenge in visual analytics is the creation of accessible, widely distributable analysis applications that bring the benefits of visual discovery to as broad a user base as possible. Moreover, to support the role of visualization in the knowledge creation process, it is advantageous to allow users to describe the reasoning strategies they employ while interacting with analytic environments. We introduce an application suite called the scalable reasoning system (SRS), which provides Web-based and mobile interfaces for visual analysis. The service-oriented analytic framework that underlies SRS provides a platform for deploying pervasive visual analytic environments across an enterprise. SRS represents a ldquolightweightrdquo approach to visual analytics whereby thin client analytic applications can be rapidly deployed in a platform-agnostic fashion. Client applications support multiple coordinated views while giving analysts the ability to record evidence, assumptions, hypotheses and other reasoning artifacts. We describe the capabilities of SRS in the context of a real-world deployment at a regional law enforcement organization.

[1]  Deborah A. Payne,et al.  Turning the Bucket of Text into a Pipe , 2005, INFOVIS.

[2]  Martin Wattenberg,et al.  ManyEyes: a Site for Visualization at Internet Scale , 2007, IEEE Transactions on Visualization and Computer Graphics.

[3]  John T. Stasko,et al.  Jigsaw: Supporting Investigative Analysis through Interactive Visualization , 2007, 2007 IEEE Symposium on Visual Analytics Science and Technology.

[4]  Luca Chittaro,et al.  Visualizing information on mobile devices , 2006, Computer.

[5]  William Wright,et al.  Information Triage with TRIST , 2005 .

[6]  Emilie M. Roth,et al.  Predicting Vulnerabilities in Computer-Supported Inferential Analysis under Data Overload , 2001, Cognition, Technology & Work.

[7]  Hsinchun Chen,et al.  COPLINK: managing law enforcement data and knowledge , 2003, CACM.

[8]  Prasenjit Mitra,et al.  FemaRepViz: Automatic Extraction and Geo-Temporal Visualization of FEMA National Situation Updates , 2007, 2007 IEEE Symposium on Visual Analytics Science and Technology.

[9]  Hans-Georg Pagendarm,et al.  A Prototype for a WWW-based Visualization Service , 1997, Visualization in Scientific Computing.

[10]  Marti A. Hearst Clustering versus faceted categories for information exploration , 2006, Commun. ACM.

[11]  Christian Posse,et al.  A Layered Dempster-Shafer Approach to Scenario Construction and Analysis , 2007, 2007 IEEE Intelligence and Security Informatics.

[12]  John Domingue,et al.  Visualizing Internetworked Argumentation , 2003, Visualizing Argumentation.

[13]  Simon Buckingham Shum,et al.  Modelling discourse in contested domains: A semiotic and cognitive framework , 2006, Int. J. Hum. Comput. Stud..

[14]  Stephen G. Eick,et al.  Thin Client Visualization , 2007, 2007 IEEE Symposium on Visual Analytics Science and Technology.

[15]  Bernice E. Rogowitz,et al.  An architecture for rule-based visualization , 1993, Proceedings Visualization '93.

[16]  Lucy T. Nowell,et al.  ThemeRiver: Visualizing Thematic Changes in Large Document Collections , 2002, IEEE Trans. Vis. Comput. Graph..

[17]  Desney S. Tan,et al.  FacetMap: A Scalable Search and Browse Visualization , 2006, IEEE Transactions on Visualization and Computer Graphics.

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

[19]  David S. Ebert,et al.  Real-time scalable visual analysis on mobile devices , 2008, Electronic Imaging.

[20]  Arun K. Pujari,et al.  QROCK: A quick version of the ROCK algorithm for clustering of categorical data , 2005, Pattern Recognit. Lett..

[21]  Rob Johnson Developing a Taxonomy of Intelligence Analysis Variables , 2003 .