INVISQUE: Technology and Methodologies for Interactive Information Visualization and Analytics in Large Library Collections

When a user knows exactly what they are looking for most library systems are adequate for their needs. However, when the user's information needs are ill-defined - traditional library systems prove inadequate. This is because traditional library systems are not designed to support sense making rather for information retrieval. Visual analytics is the science of analytical reasoning facilitated by interactive visualizations and visual analytics systems can support both sense making and information retrieval. In this paper, we present INVISQUE - an approach and experimental software for interactive visual search and query. INVISQUE uses an index card metaphor to display library content, organized in a way that visually integrates attributes such citations and date published, making it easy to pick out the most recent and most cited paper. It uses design techniques such as focus+context to reveal relationships between documents, while avoiding the "what-was-I-lookingfor?" problem.

[1]  B. Shneiderman,et al.  The dynamic HomeFinder: evaluating dynamic queries in a real-estate information exploration system , 1992, SIGIR '92.

[2]  Ben Shneiderman,et al.  The alphaslider: a compact and rapid selector , 1994, CHI Conference Companion.

[3]  Gary Marchionini,et al.  Information-Seeking Support Systems [Guest Editors' Introduction] , 2009, Computer.

[4]  Nawaz Khan,et al.  Information seeking behaviour model as a theoretical lens: high and low literate users behaviour process analysed , 2010, ECCE.

[5]  Amanda Spink,et al.  Human-computer interaction in information retrieval: nature and manifestations of feedback , 1998, Interact. Comput..

[6]  Peyman Oreizy,et al.  An architecture-based approach to self-adaptive software , 1999, IEEE Intell. Syst..

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

[8]  William Wong,et al.  User Behaviour in Resource Discovery: Final Report , 2009 .

[9]  S RosenblumDavid,et al.  An Architecture-Based Approach to Self-Adaptive Software , 1999 .

[10]  Ben Shneiderman,et al.  The alphaslider: a compact and rapid selector , 1994, CHI Conference Companion.

[11]  Daniel A. Keim,et al.  Visual Analytics: Definition, Process, and Challenges , 2008, Information Visualization.

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

[13]  Amanda Spink,et al.  Searching the Web: the public and their queries , 2001 .

[14]  Amanda Spink,et al.  From Highly Relevant to Not Relevant: Examining Different Regions of Relevance , 1998, Inf. Process. Manag..

[15]  Kristin A. Cook,et al.  Illuminating the Path: The Research and Development Agenda for Visual Analytics , 2005 .

[16]  Sharmin Choudhury Loculus : an ontology-based information management framework for the motion picture industry , 2010 .

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

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

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