Visual Analytics for Big Data

Visual Analytics for Big Data

Today not just the scientific communities but every organization faces mountains of data with conflicting information and every organization has the need for understanding causality. There may be several options:

 (i) throwing away the data, ignoring this need and carrying on with what we are used to

 (ii) relying on some brilliant people to discover causalities for the society

 (iii) hoping intelligence will be good enough to tell every “decision maker” what decision to make

 (iv) developing a future enabling technology that helps human analysts and decision makers discover causation relationships buried in the data.

Visual analytics , a term coined by Jim Thomas and his colleagues at the National Visualization and Analytics Center ( Illuminating the Path: The Research and Development Agenda for Visual Analytics, IEEE CS Press, 2005), has become the de facto standard process for integrating data analysis, visualization, and interaction to better understand complex systems. Visual analytics rests on the following assertions:

  • statistical methods alone cannot convey an adequate amount of information for humans to make informed decisions—hence the need for visualization;
  • algorithms alone cannot encode an adequate amount of human knowledge about relevant concepts, facts, and contexts—hence the need for interaction;
  • visualization alone cannot effectively manage levels of details about the data or priori­tize different information in the data—hence the need for analy­sis and interaction; and
  • direct interaction with data alone isn’t scalable to the amount of data available—hence the need for analysis and visualization.

If Alexander Bell (1847-1922) had invented visualization, just like he spoke into his telephone, he would have said "Mr. information, come here, I want to see you." In this era of data deluge, we are obliged to contemplate some fundamental questions, such as, What is data, information or knowledge ? What is visualization really for ? Perhaps the most important and challenging question is: Is there a systemic process or technology that can aid the causality discovery ?

Oxford e-Research Centre is an internationally-recognized research centre in visualization and visual analytics, and is developing advanced methodologies and technologies for addressing the big data problem.

To find more about our vision, please browse through the following references or simply contact us.

  • T. Hey and A. Trefethen, The Data Deluge: An e-Science Perspective , in Grid Computing: Making the Global Infrastructure a Reality (eds. F. Berman, G. Fox and T. Hey), John Wiley & Sons, 2003.
  • M. Chen, D. Ebert, H. Hagen, R. S. Laramee, R. van Liere, K.-L. Ma, W. Ribarsky, G. Scheuermann and D. Silver, Data, Information and Knowledge in Visualization , IEEE Computer Graphics and Applications , 29(1):12-19, 2009.
  • M. Chen and H. Jaenicke, An Information-theoretic Framework for Visualization , IEEE Transactions on Visualization and Computer Graphics , 16(6):1206-1215, 2010.
  • M. Chen, A. Trefethen, R. Banares-Alcantara, M. Jirotka, B. Coecke, T. Ertl and A. Schmidt, From data analysis and visualization to causality discovery , IEEE Computer , 44(10):84-87, 2011.
  • M. Chen, T. Ertl, M. Jirotka, A. Trefethen, A. Schmidt, B. Coecke and R. Banares-Alcantara, Causality discovery technology , The European Physical Journal Special Topics , 214:461-479, 2012.
  • M. Chen, L. Floridi and R. Borgo, What is Visualization Really for? 2013, arXiv:1305.5670.
  • M. Chen and L. Floridi, An analysis of information in visualisation , to appear in Synthese , Springer.