Dynamic Visualization of the Relationship Between Multiple Representations of an Abstract Information Space

abstract information space;multidimensional elements;scientific visualization;information visualization;texture;preattentive;cognition;perception

Sawant, Amit Prakash ; Dr. Christopher G. Healey, Committee Chair,Dr. Jon Doyle, Committee Member,Dr. Peter R. Wurman, Committee Member,Sawant, Amit Prakash ; Dr. Christopher G. Healey ; Committee Chair ; Dr. Jon Doyle ; Committee Member ; Dr. Peter R. Wurman ; Committee Member

University:North Carolina State University

关键词: abstract information space;    multidimensional elements;    scientific visualization;    information visualization;    texture;    preattentive;    cognition;    perception;   

Others  :  https://repository.lib.ncsu.edu/bitstream/handle/1840.16/2323/etd.pdf?sequence=1&isAllowed=y

美国|英语

来源: null

【 摘 要 】

Information visualization is the graphical presentation of large numbers of multidimen-sional elements. Effectively representing abstract collections of information and the relationships they contain presents a difficult challenge. Visualization is one solution to enable users to visually analyze and discover patterns within their data. Our objective is to design techniques to show how queries into an underlying dataset might be related. We use animation to represent the relationships between these multiple independent perspectives into the data.We present a new method that visualizes information along a spiral embedded in a plane. We use graphicalglyphsthat vary in color, spatial placement and texture properties to represent the attribute values contained in a data element. When shown together, the glyphs form visual patterns along the spiral that support exploration, facilitate discovery of data characteristics, and relationships, and highlight trends and exceptions. We implemented a linear clustering mechanism based on the values of the ranking attribute to enable encapsulation of similar objects. This encapsulation is used to show the relationships between different queries into a dataset via animations that morph between pairs of representations. The various stages of animation enable the user to rapidly identify the similarities and differences between the queries. We applied our techniques to the MovieLens recommender system, to demonstrate their applicability in a real-world application environment.