By Kawa Nazemi
This publication introduces a unique method for clever visualizations that adapts different visible variables and knowledge processing to human’s habit and given projects. Thereby a few new algorithms and techniques are brought to fulfill the human want of data and information and let a usable and engaging means of data acquisition. each one strategy and set of rules is illustrated in a replicable option to let the replica of the total “SemaVis” approach or components of it. The brought overview is scientifically well-designed and played with good enough members to validate the advantages of the tools. Beside the brought new ways and algorithms, readers may perhaps discover a refined literature evaluate in info Visualization and visible Analytics, Semantics and data extraction, and clever and adaptive structures. This ebook relies on an presented and exclusive doctoral thesis in desktop science.
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Extra resources for Adaptive Semantics Visualization
Harati, D. Tocker, N. Fitzgerald, A. Flagg, in AAAI Workshops (2011). php/WS/AAAIW11/paper/view/3944 26. W. Ahn, P. Brusilovsky, Inf. Process. Manage. 49(5), 1139 (2013). 2013. 007. com/science/article/pii/S0306457313000137 27. G. Carenini, C. Conati, E. Hoque, B. Steichen, D. Toker, J. Enns, in Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI’14 (ACM, New York, NY, USA, 2014), pp. 1835–1844. 2557141. 1145/2556288. 2557141 28. B. Steichen, O. Schmid, C. Conati, G.
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 . Direct Manipulation All the introduced visual interaction methods manipulate the visualization, either on a data-transformation level or on the level of visual transformation.
307]. One of the earliest techniques for focus plus context is the fisheye view [44, 45]. The model of “Degree of Interest”  was the pioneering foundation for the work on the fisheye views. In contrast to zoom, which is a transformation on the view level, the focus plus context interaction is categorized as transformation on data-level . 3 Visual Interaction 33 Beside the distortion techniques (fisheye views), Card et al. list filtering, selective aggregation, micro-macro readings and highlighting as selective information reduction methods for keeping the contextual area .