By Kawa Nazemi
This ebook introduces a unique strategy for clever visualizations that adapts the several visible variables and knowledge processing to human’s habit and given projects. Thereby a couple of new algorithms and techniques are brought to fulfill the human want of data and information and permit a usable and engaging approach of knowledge acquisition. each one procedure and set of rules is illustrated in a replicable solution to let the replica of the whole “SemaVis” method or elements of it. The brought assessment is scientifically well-designed and played with good enough individuals to validate some great benefits of the equipment. Beside the brought new ways and algorithms, readers may perhaps discover a refined literature evaluate in info Visualization and visible Analytics, Semantics and knowledge extraction, and clever and adaptive platforms. This booklet relies on an presented and wonderful doctoral thesis in laptop science.
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Additional info for Adaptive Semantics Visualization
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. The final step of the reference model completes the loop between human and visualizations (visual forms) [3, p.
Today’s information databases contain huge amount of data. The visualization of the whole data-set on a single visualization is often overcharging the human perception and information acquisition abilities. Dynamic queries can help to access the required information interactively, which can satisfy users’ heterogeneous needs of information acquisition [53, 54]. Dynamic queries provide a well-known and successful approach for exploring  and visually seeking vast amount of data . Visual interactive query formulations and refinements enable the reduction of the visualized information to a comprehensible and relevant scale .
The process starts with the data that may need to be preprocessed and transformed to an adequate way (indicated with the transformation arrow). After the transformation stage the “analyst” may choose to visualize the data or to use automatic analysis methods . Keim et al. does not use the term “user” in their process. It may indicate that the Visual Analytic model is a dedicated design for “analysts” with the necessary of previous knowledge about the processes or tasks (analysis). If the automatic analysis is chosen, techniques from data mining are applied to generate models from the underlying data.