Context-Aware Systems and Applications, and Nature of Computation and Communication. 8th EAI International Conference, ICCASA 2019, and 5th EAI International Conference, ICTCC 2019, My Tho City, Vietnam, November 28-29, 2019, Proceedings

Research Article

An Approach of Taxonomy of Multidimensional Cubes Representing Visually Multivariable Data

  • @INPROCEEDINGS{10.1007/978-3-030-34365-1_8,
        author={Hong Nguyen and Truong Le and Phuoc Tran and Dang Pham},
        title={An Approach of Taxonomy of Multidimensional Cubes Representing Visually Multivariable Data},
        proceedings={Context-Aware Systems and Applications, and Nature of Computation and Communication. 8th EAI International Conference, ICCASA 2019, and 5th EAI International Conference, ICTCC 2019, My Tho City, Vietnam, November 28-29, 2019, Proceedings},
        proceedings_a={ICCASA \& ICTCC},
        year={2019},
        month={12},
        keywords={Multidimensional cube Multivariable data Multivariate data Graph representing data Data visualization},
        doi={10.1007/978-3-030-34365-1_8}
    }
    
  • Hong Nguyen
    Truong Le
    Phuoc Tran
    Dang Pham
    Year: 2019
    An Approach of Taxonomy of Multidimensional Cubes Representing Visually Multivariable Data
    ICCASA & ICTCC
    Springer
    DOI: 10.1007/978-3-030-34365-1_8
Hong Nguyen1,*, Truong Le2,*, Phuoc Tran2,*, Dang Pham,*
  • 1: University of Information Technology, Vietnam National University - HCMC
  • 2: Hochiminh City Open University
*Contact email: hongnguyen1611@gmail.com, truong.lx@ou.edu.vn, phuoc.tvinh@ou.edu.vn, pvdang@ntt.edu.vn

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.