NOTE: Please refer this pdf to note down which points needed to be change marked as highlighted VIVA-Tech IJRI V1, E8 Article - 1 ">

The Comprehensive Review paper: Cube-Viz, Web based OLAP Cube Visualization Tool



EOI: 10.11242/viva-tech.01.09.10

Download Full Text here



Citation

Hardik Patil, Jay Sankhe, Rohan Waghmare, Prof. Bhavika Thakur, " The Comprehensive Review paper: Cube-Viz, Web based OLAP Cube Visualization Tool ", VIVA-IJRI Volume 1, Issue , Article 10 , pp. 1-5, 2026. Published by Computer Engineering Department, VIVA Institute of Technology, Virar, India.

Abstract

In the era of big data, decision-makers require fast, interactive, and meaningful insights from multidimensional datasets. Traditional OLAP (Online Analytical Processing) systems often demand specialized desktop tools, which limit accessibility and collaboration. CubeViz is a web based OLAP cube visualization tool designed to simplify multidimensional data analysis through an intuitive, browser-based interface. It enables users to perform OLAP operations such as slice, dice, drill-down, roll-up, and pivot directly from the web without the need for additional software installations. The system integrates backend data cubes with dynamic front-end visualization components to represent complex data relationships in a clear and interactive manner. Built using modern web technologies, CubeViz enhances data accessibility, performance, and user experience. The tool aims to support analysts and business users in discovering trends, patterns, and anomalies efficiently through interactive cube visualization, thereby facilitating real-time decision-making and strategic analysis.

Keywords

- Business Intelligence, Interactive Data Analysis, Multidimensional Data, OLAP, OLAP Cube, Web- based Analytics, 3D Visualization.

References

  1. P. Vassiliadis, “A Cube Algebra with Comparative Operations: Containment, Overlap, Distance and Usability”, arXiv preprint arXiv:2203.09390, 2022.
  2. M. Francia, P. Marcel, V. Peralta, and S. Rizzi, “Enhancing Cubes with Models to Describe Multidimensional Data”, Information Systems Frontiers, vol. 24, no. 1, pp. 31–48, 2021.
  3. S. He et al., “Data Cubes in Hand: A Design Space of Tangible Cubes for Visualizing 3D Spatio-Temporal Data in Mixed Reality”, Proceedings of the CHI Conference on Human Factors in Computing Systems (CHI '24), ACM, Art. no. 209, pp. 1–21, 2024.
  4. A. Cuzzocrea, D. Saccà, and P. Serafino, “Semantics-Aware Advanced OLAP Visualization of Multidimensional Data Cubes”, International Journal of Data Warehousing and Mining, vol. 3, no. 4, pp. 1–30, 2007.
  5. S. W. Baik, “A Study on the Generation of OLAP Data Cube Based on 3D Visualization Interaction”, Proceedings of the International Conference on Computational Science and Its Applications (ICCSA 2011), IEEE, pp. 231–234, 2011.
  6. J. Wagner Filho, C. T. Silva, W. Stuerzlinger, and L. Nedel, “Reimagining TaxiVis through an Immersive Space-Time Cube Metaphor and Reflecting on Potential Benefits of Immersive Analytics for Urban Data Exploration”, Proceedings of the IEEE Conference on Virtual Reality and 3D User Interfaces (VR), pp. 827–838, 2024.
  7. B. Gurvich and A. M. Geller, “Firefly: A Browser-Based Interactive 3D Data Visualization Tool for Millions of Data Points”, arXiv preprint arXiv:2207.13706, 2022.
  8. J. Ordonez and Z. Chen, “Exploration and Visualization of OLAP Cubes with Statistical Tests”, Workshop on Visual Analytics and Knowledge Discovery, ACM SIGKDD, pp. 1–6, 2009.
  9. B. Kuijpers and A. Vaisman, “An Algebra for OLAP”, Intelligent Data Analysis, vol. 21, no. 5, pp. 1267–1300, 2017.
  10. K. Techapichetvani and A. Datta, “Interactive Visualization for OLAP”, Computational Science and Its Applications – ICCSA 2005, vol. 3482, Springer, Berlin, Heidelberg, pp. 206–214, 2005.
  11. J. Wagner Filho et al., “Reimagining TaxiVis through an Immersive Space-Time Cube Metaphor”, IEEE Computer Graphics and Applications, 2024.
  12. B. Kuijpers and A. Vaisman, “Formal OLAP Algebra for Multidimensional Databases”, Intelligent Data Analysis, vol. 21, no. 5, pp. 1267–1300, 2017.