Download Adaptive Semantics Visualization by Kawa Nazemi PDF

By Kawa Nazemi

ISBN-10: 3319308157

ISBN-13: 9783319308159

This booklet introduces a singular strategy for clever visualizations that adapts different visible variables and knowledge processing to human’s habit and given projects. Thereby a few new algorithms and strategies are brought to meet the human desire of data and data and allow a usable and engaging approach of knowledge acquisition. each one technique and set of rules is illustrated in a replicable approach to permit the copy of the total “SemaVis” process or elements of it. The brought review is scientifically well-designed and played with good enough contributors to validate the advantages of the equipment. Beside the brought new ways and algorithms, readers may well discover a refined literature evaluate in details Visualization and visible Analytics, Semantics and knowledge extraction, and clever and adaptive platforms. This ebook relies on an presented and exotic doctoral thesis in laptop science.

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4 illustrates the differentiation and introduces further the field of Semantics Visualization, which will be described more detailed in Chap. 3. In this work we use the definition of information visualization as defined by Card et al. [3]. Thus Visual Analytics makes use of information visualization for the visual stages [11], we use the term information visualization for the visualization aspects of visual analytics too. When describing Visual Analytics systems, our focus will be the way how information is visualized and human are interacting with and perceiving the visualizations.

1) A Data Table combines relations with their describing metadata [3]. A data table is represented by rows, which contains variables as set of values in the tables and cases as set values for each variable. In context of data tables they introduce a categorization of the data variables and their possible sequences. They propose that there are three basic types of variables, nominal, ordinal, and quantitative. Nominal variables are unordered sets (are only = or = to other values), ordinal variables are ordered sets (obeys a < relation ), and quantitative variables are numeric ranges (can do arithmetic on them) [3, pp.

A. Kobsa, J. Koenemann, W. Pohl, Knowl. Eng. Rev. 16, 111 (2001) 38. D. Parra, P. Brusilovsky, C. Trattner, in Proceedings of the 19th International Conference on Intelligent User Interfaces, IUI’14 (ACM, New York, NY, USA, 2014), pp. 235–240. doi:10. 2557542. 2557542 39. J. E. Sucar, in ENC’05: Proceedings of the Sixth Mexican International Conference on Computer Science (IEEE Computer Society, Washington, DC, USA, 2005), pp. 2–9. doi:10. 7 40. W. Ahn, P. Brusilovsky, Inf. Vis. 8(3), 180 (2009).

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