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Visualization of Time-Oriented Data

Visualization of Time-Oriented Data


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About the Book

Time is an exceptional data dimension due to its importance and unique influence. This implies that special methods are needed for dealing with time in data sets in order to take advantage of these specifics. Visual methods are a key part of this tool set allowing for presentation, analysis, and exploration of temporal data sets. Visualisation of Time-Oriented Data provides an overview of the field of visualisation techniques for time-oriented data, but even more importantly analyses the specifics and similarities of the investigated techniques in order to identify clusters for structuring and making the field manageable. It also analyses the techniqes uniformly along different sets of attributes making it easy to grasp their main characteristics and support researchers as well as developers in selecting appropriate techniques for their demands. The theoretic foundations of visualization itself are also discussed, the data dimension time, tasks related to time-oriented data, possible mappings of time, various examples of early (pre-computer age) representation techniques, and a conceptual framework for visually analyzing time-oriented data.

Review :
Fernando Berzal - ACM Computing Reviews 16 July 2012 There is no doubt about the importance of visualization in data analysis, whether for examining the unknown and suggesting novel approaches to explore, for confirming yet-to-be-proved hypotheses and abandoning future dead ends, or just for supporting someone s ideas with visual aids in a presentation. In most situations, the data being analyzed includes a temporal dimension whose proper treatment might boost (or bust) the resolution of a given data analysis task. Hence a systematic treatise on the visualization of time-oriented data is welcome. Four researchers from Vienna University of Technology in Austria and the University of Rostock in Germany have compiled this extensive catalog of visualization techniques where the temporal dimension plays a major role. Their book contains one-page descriptions of 101 different ways to represent the temporal dimension of data in two or three dimensions. The catalogued techniques include literally dozens of different ways to plot time series. Some have been devised for highlighting parallelisms among different time series when analyzing multivariate data, whereas others were designed for bringing the periodic nature of time into the spotlight. Many of them have been incorporated into data visualization tools for making efficient use of the limited screen real estate and some allow for the visualization of spatiotemporal data, where appropriate. The page devoted to each technique is beautifully illustrated with a relevant diagram or screen shot. Each page also includes a concise textual description of the rationale behind the technique under analysis and, almost as a footnote, the most pertinent bibliographic reference (in rare occasions, a handful of them). The survey of visualization techniques comprises the second half of this nicely illustrated book. The first half tries to cover the intellectual foundation behind the visualization techniques themselves. A short introduct


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Product Details
  • ISBN-13: 9780857290809
  • Publisher: Springer
  • Publisher Imprint: Springer
  • Height: 234 mm
  • Weight: 426 gr
  • ISBN-10: 0857290800
  • Publisher Date: 31 May 2011
  • Binding: Paperback
  • Spine Width: 16 mm
  • Width: 156 mm


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