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14-September-2008 12:50:30 - Data visualization The research process from data to visualization. The research process from data to visualization.1 Data visualization is the study of the visual representation of data, defined as information which has been abstracted in some schematic form, including attributes or variables for the units of information.2 Contents 1 Overview 2 History 3 Data visualization scope 4 Related fields 4.1 Data acquisition 4.2 Data analysis 4.3 Data governance 4.4 Data management 4.5 Data mining 5 See also 6 References 7 Further reading 8 External links Overview The main goal of data visualization is to communicate information clearly and effectively through graphical means. It doesn't mean that data visualization needs to look boring to be functional or extremely sophisticated to look beautiful. To convey ideas effectively, both aesthetic form and functionality need to go hand in hand, providing insights into a rather sparse and complex data set by communicating its key-aspects in a more intuitive way. Yet designers often tend to discard the balance between design and function, creating gorgeous data visualizations which fail to serve its main purpose - communicate information.3 Data visualization is closely related to Information graphics, Information visualization, Scientific visualization and Statistical graphics. According to Frits Post 2003 data visualization is currently a very active and vital area of research, teaching and development. The term unites the established field of scientific visualization and the more recent field of information visualization.4 History The origins of this field are in the early days of computer graphics in the 1950s, when the first graphs and figures were generated by computers. A strong impulse was given to the field by the appearance, in 1987, of the NSF report Visualization in Scientific Computing ed by Bruce H. McCormick, Thomas A. DeFanti and Maxine D. Brown. In this report the need for new computer-based visualization techniques was stressed. With the rapid increase of computing power, larger and more complex numerical models were developed, resulting in the generation of huge numerical data sets. Also, large data sets were generated by data acquisition devices such as medical scanners and microscopes, and data was collected in large databases containing text, numerical information and multimedia information. Advanced computer graphics techniques were needed to process and visualize these massive data sets.4 The phrase Visualization in Scientific Computing which turned into Scientific Visualization was used initially to refer to visualization as a part of a process of scientific computing: the use of computer modelling and simulation in scientific and engineering practice. More recently, visualization is increasingly also concerned with data from other sources, including large and heterogeneous data collections found in business and finance, administration, digital media, etc. A new research area called Information Visualization was launched in the early 1990s, to support analysis of abstract and heterogeneous data sets in many application areas. Therefore, the phrase Data Visualization is gaining acceptance to include both the scientific and information visualization fields.4 Since then data visualization is an evolving concept whose boundaries are continually expending and, as such, is best defined in terms of loose generalizations. It referes to the more technologically advanced techniques, which allow visual interpretation of data through the representation, modelling and display of solids, surfaces, properties and animations, involving the use of graphics, image processing, computer vision and user interfaces. It encompasses a much broader range of techniques then specific techniques as solid modelling.5 Data visualization scope There are different approaches on the scope of data visualization. One common focus is on information presentation. For example Michael Friendly 2008 presumes two main parts of data visualization: statistical graphics, and thematic cartography.2 In this line the Data Visualization: Modern Approaches 2007 article gives an overview of seven subjects of data visualisation:6 Mindmaps Displaying News Displaying Data Displaying connections Displaying websites Articles Resources Tools and Services All these subjects are all close related to graphic design and information reprentation. On the other hand, from a computer science perspective, Frits H. Post 2002 categorized the field into a number of sub-fields: 4 Visualization Algorithms and Techniques Volume Visualization Information Visualization Multiresolution Methods Modelling Techniques and Interaction Techniques and Architectures The success of data visualization is due to the soundness of the basic idea behind it: the use of computer-generated images to gain insight and knowledge from data and its inherent patterns and relationships. A second premise is the utilization of the broad bandwidth of the human sensory system in steering and interpreting complex processes, and simulations involving data sets from diverse scientific disciplines and large collections of abstract data from many sources. These concepts are extremely important and have a profound and widespread impact on the methodology of computational science and engineering, as well as on management and administration. The interplay between various application areas and their specific problem solving visualization techniques is emphasized in this book. 4 Related fields Data acquisition Data acquisition is the sampling of the real world to generate data that can be manipulated by a computer. Sometimes abbreviated DAQ or DAS, data acquisition typically involves acquisition of signals and waveforms and processing the signals to obtain desired information. The components of data acquisition systems include appropriate sensors that convert any measurement parameter to an electrical signal, which is acquired by data acquisition hardware. Data analysis Data analysis is the process of looking at and summarizing data with the intent to extract useful information and develop conclusions. Data analysis is closely related to data mining, but data mining tends to focus on larger data sets, with less emphasis on making inference, and often uses data that was originally collected for a different purpose. In statistical applications, some people divide data analysis into descriptive statistics, exploratory data analysis and confirmatory data analysis, where the EDA focuses on discovering new features in the data, and CDA on confirming or falsifying existing hypotheses. Types of data analysis are: Exploratory data analysis EDA: an approach to analyzing data for the purpose of formulating hypotheses worth testing, complementing the tools of conventional statistics for testing hypotheses. It was so named by John Tukey. Qualitative data analysis QDA or qualitative research is the analysis of non-numerical data, for example words, photographs, observations, etc.. Data governance Data governance encompasses the people, processes and technology required to create a consistent, enterprise view of an organisation's data in order to: Increase consistency confidence in decision making Decrease the risk of regulatory fines Improve data security Maximize the income generation potential of data Designate accountability for information quality Data management Data management comprises all the academic disciplines related to managing data as a valuable resource. The official definition provided by DAMA is that Data Resource Management is the development and execution of architectures, policies, practices and procedures that properly manage the full data lifecycle needs of an enterprise. This definition is fairly broad and encompasses a number of professions which may not have direct technical contact with lower-level aspects of data management, such as relational database management. Data mining Data mining is the process of sorting through large amounts of data and picking out relevant information. It is usually used by business intelligence organizations, and financial analysts, but is increasingly being used in the sciences to extract information from the enormous data sets generated by modern experimental and observational methods. It has been described as the nontrivial extraction of implicit, previously unknown, and potentially useful information from data7 and the science of extracting useful information from large data sets or databases.8 Data mining in relation to enterprise resource planning is the statistical and logical analysis of large sets of transaction data, looking for patterns that can aid decision making.9 See also Software programs/ visualization applications/graphics toolkit Visalix Eye-Sys Ferret Data Visualization and Analysis GGobi IBM OpenDX OpenLink AJAX Toolkit ParaView Smile software StatSoft Visifire VTK Data Desk Organizations Interactive Data Visualization, Inc. Dundas Data Visualization, Inc. National Oceanographic Data Center References ^ National Visualization and Analytics Center. Retrieved 1 Juli 2008. ^ a b Michael Friendly 2008. Milestones in the history of thematic cartography, statistical graphics, and data visualization. ^ Data Visualization and Infographics in: Graphics, Monday Inspiration, January 14th, 2008. ^ a b c d e Frits H. Post, Gregory M. Nielson and Georges-Pierre Bonneau 2002. Data Visualization: The State of the Art. ^ Paul Reilly, S. P. Q. Rahtz eds. 1992. Archaeology and the Information Age: A Global Perspective. p.92. ^ Data Visualization: Modern Approaches. in: Graphics, August 2nd, 2007 ^ W. Frawley and G. Piatetsky-Shapiro and C. Matheus Fall 1992. Knowledge Discovery in Databases: An Overview. AI Magazine: pp. 213-228. ISSN 0738-4602. ^ D. Hand, H. Mannila, P. Smyth 2001. Principles of Data Mining. MIT Press, Cambridge, MA. ISBN 0-262-08290-X. ^ Ellen Monk, Bret Wagner 2006. Concepts in Enterprise Resource Planning, Second ion. Thomson Course Technology, Boston, MA. ISBN 0-619-21663-8. Further reading Chandrajit Bajaj, Bala Krishnamurthy 1999. Data Visualization Techniques. William S. Cleveland 1993. Visualizing Data. Hobart Press. William S. Cleveland 1994. The Elements of Graphing Data. Hobart Press. Michael Friendly 2008. Reader Milestones in the History of Thematic Cartography, Statistical Graphics and Data Visualization Alexander N. Gorban, Balázs Kégl and Andrey Zinovyev 2007. http://www.amazon.com/Principal-Manifolds-Visualization-Computational-Engineering/dp/3540737499 Principal Manifolds for Data Visualization and Dimension Reduction. John P. Lee and Georges G. Grinstein eds. 1994. Database Issues for Data Visualization: IEEE Visualization '93 Workshop, San Diego. Peter R. Keller and Mary Keller 1993. Visual Cues: Practical Data Visualization. Frits H. Post, Gregory M. Nielson and Georges-Pierre Bonneau 2002. Data Visualization: The State of the Art. External links Wikimedia Commons has media related to: Data visualization Milestones in the History of Thematic Cartography, Statistical Graphics, and Data Visualization, An illustrated chronology of innovations by Michael Friendly and Daniel J. Denis. Prefuse is a set of software tools for creating rich interactive data visualizations. v d e Visualization Fields Creative visualization · Chemical imaging · Crime mapping · Data visualization · Educational visualization · Flow visualization · Geovisualization · Information visualization · Medical imaging · Music visualization · Product visualization · Scientific visualization · Software visualization · Volume visualization Image types Chart · Computer graphics · Diagram · Graph of a function · Ideogram · Illustration · Information graphics · Map · Photograph · Pictogram · Statistical graphics · Table · Technical drawing Experts Jacques Bertin · Stuart Card · Thomas A. DeFanti · Michael Friendly · Nigel Holmes · Jock D. Mackinlay · Michael Maltz · Bruce H. McCormick · Charles Joseph Minard · Otto Neurath · William Playfair · Clifford A. Pickover · Arthur H. Robinson · Lawrence J. Rosenblum · Adolphe Quetelet · George G. Robertson · Ben Shneiderman · Edward Tufte Related topics Cartography · Computer graphics · Graph drawing · Graphic design · Imaging science · Information science · Mental visualisation · Neuroimaging · Spatial analysis · Visual analytics · Visual communication · Visual perception Retrieved from http://en..org/wiki/Data_visualization Categories: Visualization graphic | Data analysis | Data collection | Data management | Data mining | Information technology governance Views Article Discussion this page History Personal tools Log in / create account Navigation Main page Contents Featured content Current events Random article Search Go Search Interaction Community portal Recent changes Contact Donate to Help Toolbox What links here Related changes Upload file Special pages Printable version Permanent link Cite this page Languages Nederlands This page was last modified on 12 September 2008, at 07:34
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