Research Article
Two-Stage Approach to Classifying Multidimensional Cubes for Visualization of Multivariate Data
@INPROCEEDINGS{10.1007/978-3-030-06152-4_7, author={Hong Nguyen and Thuan Pham and Tuyet Nguyen and Anh Tran and Phuoc Tran and Dang Pham}, title={Two-Stage Approach to Classifying Multidimensional Cubes for Visualization of Multivariate Data}, proceedings={Context-Aware Systems and Applications, and Nature of Computation and Communication. 7th EAI International Conference, ICCASA 2018, and 4th EAI International Conference, ICTCC 2018, Viet Tri City, Vietnam, November 22--23, 2018, Proceedings}, proceedings_a={ICCASA \& ICTCC}, year={2019}, month={1}, keywords={Visualization Multidimensional cube Multivariate data Visual analytics}, doi={10.1007/978-3-030-06152-4_7} }- Hong Nguyen
Thuan Pham
Tuyet Nguyen
Anh Tran
Phuoc Tran
Dang Pham
Year: 2019
Two-Stage Approach to Classifying Multidimensional Cubes for Visualization of Multivariate Data
ICCASA & ICTCC
Springer
DOI: 10.1007/978-3-030-06152-4_7
Abstract
Visualization of multivariate data is a big challenge to problems of visual analytics. A system of data visualization is composed of visual mapping stage and visual display stage. The stage of visual mapping converts data to graph and the stage of visual display shows the graph on screen in accordance with human’s retinal perception which is specified by visual features and Gestalt’s laws. Based on data characteristics, multidimensional cubes representing multivariate data are classified as non-spatial multidimensional cube for non-spatial data, spatial multidimensional cube for spatio-temporal data, spatio-temporal multidimensional cube for movement data, and 3D-spatio-temporal multidimensional cube for flight data. For a visualization system responding human’s retinal perception, multidimensional cubes have to enable analysts to answer elementary questions concerning individual values, variation questions concerning part of data or overall data, and relation questions resulting in the correlation among attributes.
