Context adaptive visualization for effective business intelligence

Organizations nowadays invest heavily in business intelligence (BI) systems to get insights from their increasingly large volumes of complex data, support decision making and achieve competitive advantages. The visualization capability of a BI system in terms of developing effective visualizations for addressing business problems is crucial to the success of BI. With the rapid advances achieved in the domain of information visualization, many existing visualization techniques/applications can provide reasonable support for particular paradigms, problem domains, and data types. However, they are still weak for supporting multi-paradigm, multi-domain problems and maintaining visualization effectiveness under dynamic contexts. BI systems need to include visualization subsystems or be used together with separate visualization systems that offer flexible support for creating, manipulating and transforming visualization solutions. In this paper, we discuss business visualization context and propose and implement a context adaptive visualization framework. Furthermore, we demonstrate the framework implementation through a sequence of context-driven illustrations.

[1]  Jeffrey Heer,et al.  Software Design Patterns for Information Visualization , 2006, IEEE Transactions on Visualization and Computer Graphics.

[2]  Anind K. Dey,et al.  Understanding and Using Context , 2001, Personal and Ubiquitous Computing.

[3]  David Sundaram,et al.  A Flexible Approach for Visualization Development , 2010, 2010 Sixth International Conference on Signal-Image Technology and Internet Based Systems.

[4]  Catherine Plaisant,et al.  The challenge of information visualization evaluation , 2004, AVI.

[5]  David Sundaram,et al.  Purposeful Visualization , 2011, 2011 44th Hawaii International Conference on System Sciences.

[6]  Robert Spence,et al.  Information Visualization: Design for Interaction (2nd Edition) , 2007 .

[7]  Robert Kosara,et al.  Implied dynamics in information visualization , 2010, AVI.

[8]  Guang Chen,et al.  Visual analytics in the pharmaceutical industry , 2004, IEEE Computer Graphics and Applications.

[9]  Chaomei Chen,et al.  Top 10 Unsolved Information Visualization Problems , 2005, IEEE Computer Graphics and Applications.

[10]  Ken Brodlie,et al.  Gaining understanding of multivariate and multidimensional data through visualization , 2004, Comput. Graph..

[11]  Ben Shneiderman,et al.  Readings in information visualization - using vision to think , 1999 .

[12]  Mikael Jern Visual intelligence-turning data into knowledge , 1999, 1999 IEEE International Conference on Information Visualization (Cat. No. PR00210).

[13]  Ed H. Chi,et al.  A taxonomy of visualization techniques using the data state reference model , 2000, IEEE Symposium on Information Visualization 2000. INFOVIS 2000. Proceedings.