By Kawa Nazemi

ISBN-10: 3319308157

ISBN-13: 9783319308159

ISBN-10: 3319308165

ISBN-13: 9783319308166

This publication introduces a unique procedure for clever visualizations that adapts different visible variables and knowledge processing to human’s habit and given initiatives. Thereby a couple of new algorithms and strategies are brought to meet the human want of data and data and allow a usable and tasty means of knowledge acquisition. every one procedure and set of rules is illustrated in a replicable method to permit the copy of the full “SemaVis” method or elements of it. The brought overview is scientifically well-designed and played with good enough contributors to validate the advantages of the tools. Beside the brought new ways and algorithms, readers may perhaps discover a refined literature assessment in details Visualization and visible Analytics, Semantics and knowledge extraction, and clever and adaptive platforms. This ebook relies on an offered and special doctoral thesis in machine science.

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1) in tasks like search or pattern detection and others for controlled 20 2 Information Visualization processing (see Sect. 2) [3, p. 25] the reference model itself does not propagate this separation. It focuses more on a general transformation of data tables and their sequential characteristics to visual structures. Visual structures may appear as Spatial Substrates, Marks, Connection and Enclosures, Retinal Properties, and Temporal Encodings, whereas the transformation encloses the entire spectrum of visual structures.

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). com/cgi/content/short/ 8/3/167 14 1 Introduction 41. D. Toker, C. Conati, G.

Treisman and others used this test to identify a list of preattentive visual features [17–19]. Further they detected that some of the visual features are asymmetric, while others are symmetric. A circle with a line (as a visual feature) in a sea of circles can be processed preattentively, while a circle without a line in a sea with circle with lines is not preattentively processed [15, 17, 19]. 5 illustrates the difference between symmetric and asymmetric visual features. Treisman and Souther explained the phenomenon of preattentive visual processing using a model of low-level human vision made up of a feature map and a master map of locations [18].

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Adaptive Semantics Visualization by Kawa Nazemi


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