By Kawa Nazemi
This e-book introduces a unique process 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 meet the human desire of knowledge and data and permit a usable and tasty manner of data acquisition. each one procedure and set of rules is illustrated in a replicable solution to let the copy of the total “SemaVis” process or elements of it. The brought evaluate is scientifically well-designed and played with good enough individuals to validate the advantages of the tools. Beside the brought new techniques and algorithms, readers might discover a subtle literature overview in details Visualization and visible Analytics, Semantics and knowledge extraction, and clever and adaptive structures. This publication relies on an presented and uncommon doctoral thesis in desktop science.
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Additional info for Adaptive Semantics Visualization
3]. Thus Visual Analytics makes use of information visualization for the visual stages , 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. Amplifying cognition and acquiring knowledge [3, 11] with the use of human’s visual perception is essential for this work, whereas the automated data processing and data analytics methods are not in scope of this work.
This classification is according to Ware’s model on the highest problem solving loop of visual interactions. The mantra further correlates seven data types to seven tasks on the highest level of abstraction. Cockburn et al. enhanced the interaction aspects of the Visual Information Seeking Mantra to survey and categorize visualizations . They defined “overview plus context” as Spatial Separation between focused information entities and contextual information. The “zooming” interaction was reduced to the temporal separation of entities, whereas “focus plus context” minimizes the seam within the contextual information.
By changing the size of a zoomed object. In contrast to the ordinary zoom, semantic zoom uncovers detailed information to encompass the context and meaning of a zoomed target . Semantic zoom is for example recently used in ontology visualization for reducing the complexity of large ontologies . g. by data transformations, visual mappings, and view transformations. In these types of interactions users acts directly with visualizations and change the view. In contrast to the manipulating interaction techniques, animation does not provide the user with manipulation functionalities.