By Kawa Nazemi
This publication introduces a singular procedure for clever visualizations that adapts the various 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 knowledge and information and allow a usable and engaging manner of data acquisition. each one procedure and set of rules is illustrated in a replicable option to allow the replica of the full “SemaVis” method or components of it. The brought overview is scientifically well-designed and played with good enough individuals to validate some great benefits of the tools. Beside the brought new techniques and algorithms, readers might discover a subtle literature assessment in info Visualization and visible Analytics, Semantics and data extraction, and clever and adaptive structures. This booklet is predicated on an provided and wonderful doctoral thesis in desktop science.
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Additional info for Adaptive Semantics Visualization
2 provides an overview of the proposed task, manipulation views and interactions that are related to each other. Another approach, which correlates low-level interactions with visualization tasks, was proposed by Chuah and Roth . They summarized their “basic visualization interactions” as a set of low-level-interactions with the attributes input, output, and operation and abstracted them to three basic visualization tasks [60, p. 31]. g. add, delete or derive attributes. The higher level considers “Set Operations”, which refers to operations on sets, which may have group characteristics.
6] Based on the type of data to be visualized they define information visualization as: The use of computer-supported, interactive, visual representations of abstract data to amplify cognition. [3, p. 6] The main difference in this definition is the term “abstract data”, which is related to the fact that no obvious spatial mappings can be assigned to the data. Without a spatial abstraction, one challenge is the problem of rendering the data into an effective visual [3, p. 7]. To face the mapping problem of raw data to visual forms Card et al.
Further we will slightly change an existing classification and introduce the data types based on this classification. The chapter will conclude with a section about technique and methods for visualizing information. This section will follow the same procedure and introduces first various existing classifications. Here again we will see that the proposed classifications are not appropriate for our purposes and will combine existing classifications to have a baseline for introducing the visualization techniques.
Adaptive Semantics Visualization by Kawa Nazemi