An Analysis of Information in Visualisation

Philosophers have relied on visual metaphors to analyse ideas and explain their theories at least since Plato. Descartes is famous for his system of axes, and Wittgenstein for his first design of truth table diagrams. Today, visualisation is a form of ‘computer-aided seeing’ information in data. Hence, information is the fundamental ‘currency’ exchanged through a visualisation pipeline. In this article, we examine the types of information that may occur at different stages of a general visualization pipeline. We do so from a quantitative and a qualitative perspective. The quantitative analysis is developed on the basis of Shannon’s information theory. The qualitative analysis is developed on the basis of Floridi’s taxonomy in the philosophy of information. We then discuss in detail how the condition of the ‘data processing inequality’ can be broken in a visualisation pipeline. This theoretic finding underlines the usefulness and importance of visualisation in dealing with the increasing problem of data deluge. We show that the subject of visualisation should be studied using both qualitative and quantitative approaches, preferably in an interdisciplinary synergy between information theory and the philosophy of information.

[1]  Edward R. Tufte,et al.  The Visual Display of Quantitative Information , 1986 .

[2]  P. Fayers,et al.  The Visual Display of Quantitative Information , 1990 .

[3]  Claude E. Shannon,et al.  The lattice theory of information , 1953, Trans. IRE Prof. Group Inf. Theory.

[4]  Melanie Tory,et al.  Rethinking Visualization: A High-Level Taxonomy , 2004 .

[5]  L. Floridi.  Information: A Very Short Introduction , 2010 .

[6]  Hans-Peter Kriegel,et al.  Visualization Techniques for Mining Large Databases: A Comparison , 1996, IEEE Trans. Knowl. Data Eng..

[7]  Min Chen,et al.  Data, Information, and Knowledge in Visualization , 2009, IEEE Computer Graphics and Applications.

[8]  Gordon B. Davis,et al.  Management information systems : conceptual foundations, structure, and development , 1985 .

[9]  O. Sacks,et al.  The Man Who Mistook His Wife for a Hat and Other Clinical Tales , 1985 .

[10]  Cristian S. Calude.  Algorithmic Information Theory: Open Problems , 1996, J. Univers. Comput. Sci..

[11]  Samuel B. Williams,et al.  ASSOCIATION FOR COMPUTING MACHINERY , 2000 .

[12]  J. Jastrow.  The Mind's Eye. , 1899 .

[13]  L. Floridi.  Perception and Testimony as Data Providers , 2019, The Logic of Information.

[14]  Sang Joon Kim,et al.  A Mathematical Theory of Communication , 2006 .

[15]  Min Chen,et al.  An Information-theoretic Framework for Visualization , 2010, IEEE Transactions on Visualization and Computer Graphics.

[16]  Clayton Lewis,et al.  A problem-oriented classification of visualization techniques , 1990, Proceedings of the First IEEE Conference on Visualization: Visualization `90.

[17]  Andreas Buja,et al.  Interactive High-Dimensional Data Visualization , 1996 .

[18]  Luciano Floridi,et al.  What is the Philosophy of Information , 2002 .

[19]  Steven K. Feiner,et al.  Visual task characterization for automated visual discourse synthesis , 1998, CHI.

[20]  L. Floridi.  What is the Philosophy of Information ? [ forthcoming in Metaphilosophy ] , 2001 .

[21]  Gregory J. Chaitin,et al.  A recent technical report , 1974, SIGA.

[22]  S. Zeki.  The Mind’s Eye , 2011, Cerebrum : the Dana forum on brain science.

[23]  Ben Shneiderman,et al.  The eyes have it: a task by data type taxonomy for information visualizations , 1996, Proceedings 1996 IEEE Symposium on Visual Languages.

[24]  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.

[25]  Thomas M. Cover,et al.  Elements of Information Theory , 2005 .

[26]  Min Chen,et al.  Temporal Visualization of Boundary‐based Geo‐information Using Radial Projection , 2011, Comput. Graph. Forum.

[27]  A KeimDaniel,et al.  Visualization Techniques for Mining Large Databases , 1996 .

[28]  Peter Checkland,et al.  Soft Systems Methodology in Action , 1990 .

[29]  J ChaitinGregory.  A Theory of Program Size Formally Identical to Information Theory , 1975 .