Abstract
In data visualization, graphs representing multivariable data on multidimensional coordinates shaped cubes enable human to understand better the significance of data. There are various types of cubes for representing different datasets. The paper aims at classifying kinds of cubes to enable human to design cubes representing multivariable datasets. Mathematically, the functional relations among five groups of variables result in the way to structure cubes. The paper classifies cubes as three kinds by the characteristics of datasets, including non-space, 2D-space, and 3D-space multidimensional cubes. The non-space multidimensional cubes are applied for non-space multivariable datasets with variables of objects, attributes, and times. The 2D-space multidimensional cubes are applied for the datasets of movers or objects located on ground at time units. The 3D-space multidimensional cubes are applied for the datasets of flyers or objects positioned in elevated space at time units. The correlation in space and/or time shown on the cubes enables human to discover new valuable information.
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This paper is sponsored by Hochiminh City Open University.
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Thi Nguyen, H., Xuan Le, T., Vinh Tran, P., Van Pham, D. (2019). An Approach of Taxonomy of Multidimensional Cubes Representing Visually Multivariable Data. In: Vinh, P., Rakib, A. (eds) Context-Aware Systems and Applications, and Nature of Computation and Communication. ICCASA ICTCC 2019 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 298. Springer, Cham. https://doi.org/10.1007/978-3-030-34365-1_8
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