MULTISCALE VISUALIZATION OF RELATIONAL DATABASES USING LAYERED ZOOM TREES AND PARTIAL DATA CUBES

Baoyuan Wang, Gang Chen, Jiajun Bu, Yizhou Yu

Abstract

The analysis and exploration necessary to gain deep understanding of large databases demand an intuitive and informative human-computer interface. In this paper, we present a visualization system with a client-server architecture for multiscale visualization of relational databases. The visual interface on the client supports web-based remote access. We use zoom trees to represent the entire history of a zooming process that reveals multiscale details. Every path in a zoom tree represents a zoom path and every node in the tree can have an arbitrary number of subtrees to support arbitrary branching and backtracking. Zoom trees are seamlessly integrated with a table-based overview using ”hyperlinks” embedded in the table. To support fast query processing on the server, we further develop efficient GPU-based parallel algorithms for online data cubing and CPU-based data clustering. Also, a user study was conducted to evaluate the effectiveness of our design.

References

  1. Proclarity analytics 6 2006. from: http://www.proclarity. com/products/proclarity analytics 6.asp.
  2. Report portal 2006:zero-footprint olap web client solution xmla consluting. from:. http://www.reportportal.com.
  3. Allison, W., Chris, O., Alexander, A., Michael, C., Vuk, E., Mark, L., Mybrid, S., and Michael, S. (2001). Datasplash: A direct manipulation environment for programming semantic zoom visualizations of tabular data. Journal of Visual Languages & Computing, 12:551-571.
  4. Antis, J., Eick, S., and Pyrce, J. (1996). Visualizing the structure of large relational databases. Software, IEEE, 13(1):72-79.
  5. Bederson, B. B. and Hollan, J. D. (1994). Pad++: a zooming graphical interface for exploring alternate interface physics. In UIST 7894: ACM symposium on user interface software and technology.
  6. Chaudhuri, S. and Dayal, U. (1997). An overview of data warehousing and OLAP technology. SIGMOD Record, 26:65-74.
  7. CUDA (2008). Nvidia cuda (compute unified device architecture) programming guide 2.0. http://developer.nvidia.com/object/cuda.html.
  8. Ellis, G. and Dix, A. (2007). A taxonomy of clutter reduction for information visualisation. IEEE Transactions on Visualization and Computer Graphics, 13(6).
  9. Fua, Y.-H., Ward, M. O., and Rundensteiner, E. A. (1999). Hierarchical parallel coordinates for exploration of large datasets. In IEEE conference on Visualization 7899.
  10. Gray, J., Chaudhuri, S., Bosworth, A., Layman, A., Reichart, D., Venkatrao, M., Pellow, F., and Pirahesh, H. (1997). Data cube: A relational aggregation operator generalizing group-by, cross-tab and sub-totals. Data Mining and Knowledge Discovery, 1:29-54.
  11. Han, J., Chen, Y., Dong, G., Pei, J., Wah, B., Wang, J., and Cai, Y. (2005). Stream cube: An architecture for multi-dimensional analysis of data streams. Distributed and Parallel Databases, 18(2):173-197.
  12. Han, J., Pei, J., Dong, G., and Wang, K. (2001). Efficient computation of iceberg cubes with complex measures. In SIGMOD.
  13. Harris, M. (2008). Optimizing parallel reduction in cuda. http://developer.download.nvidia.com/compute/cuda /sdk/website/projects/reduction/doc/reduction.pdf.
  14. Inc, B. (2007). Microsoft Excel 2007 Charts & Tables Quick Reference Guide.
  15. Kesaraporn, T., Amitava, D., and Robyn, O. (2004). Hddv: Hierarchical dynamic dimensional visualization for multidimensional data. In IASTED 782004: International Conference on Databases and Applications, pages 157-162.
  16. Kreuseler, M. and Schumann, H. (1999). Information visualization using a new focus+context technique in combination with dynamic clustering of information space. In NPIVM 7899: the 1999 workshop on new paradigms in information visualization and manipulation.
  17. Maniatis, A. S., Vassiliadis, P., Skiadopoulos, S., and Vassiliou, Y. (2003). Advanced visualization for olap. In DOLAP 7803: 6th ACM international workshop on Data warehousing and OLAP.
  18. Mansmann, S. and Scholl, M. H. (2007). Exploring olap aggregates with hierarchical visualization techniques. In SAC 7807: ACM symposium on Applied computing.
  19. Peng, W., Ward, M. O., and Rundensteiner, E. A. (2004). Clutter reduction in multi-dimensional data visualization using dimension reordering. In INFOVIS 7804: Proceedings of the IEEE Symposium on Information Visualization.
  20. Rao, R. and Card, S. K. (1994). The table lens: merging graphical and symbolic representations in an interactive focus + context visualization for tabular information. In CHI 7894: SIGCHI conference on Human factors in computing systems.
  21. Rundensteiner, E. A., Ward, M. O., Yang, J., and Doshi, P. R. (2002). Xmdvtool: visual interactive data exploration and trend discovery of high-dimensional data sets. In SIGMOD 7802: 2002 ACM SIGMOD international conference on Management of data.
  22. Shalom, S. A., Dash, M., and Tue, M. (2008). Efficient k-means clustering using accelerated graphics processors. In DaWaK 7808: 10th international conference on Data Warehousing and Knowledge Discovery.
  23. Stolte, C., Tang, D., and Hanrahan, P. (2002). Polaris: A system for query, analysis, and visualization of multidimensional relational databases. IEEE Trans. on Visualization and Computer Graphics, 8:52-65.
  24. Stolte, C., Tang, D., and Hanrahan, P. (2003). Multiscale visualization using data cubes. IEEE Trans. on Visualization and Computer Graphics, 9:176-187.
  25. Techapichetvanich, K. and Datta, A. (2005). Interactive visualization for olap. In ICCSA 782005: International Conference on Computational Science and its Applications Part III, pages 206-214.
  26. Weijia Xu, K. P. (2008). On interactive visualization with relational database. In InfoVis'2008, Poster.
Download


Paper Citation


in Harvard Style

Wang B., Chen G., Bu J. and Yu Y. (2010). MULTISCALE VISUALIZATION OF RELATIONAL DATABASES USING LAYERED ZOOM TREES AND PARTIAL DATA CUBES . In Proceedings of the International Conference on Imaging Theory and Applications and International Conference on Information Visualization Theory and Applications - Volume 1: IVAPP, (VISIGRAPP 2010) ISBN 978-989-674-027-6, pages 101-111. DOI: 10.5220/0002829301010111


in Bibtex Style

@conference{ivapp10,
author={Baoyuan Wang and Gang Chen and Jiajun Bu and Yizhou Yu},
title={MULTISCALE VISUALIZATION OF RELATIONAL DATABASES USING LAYERED ZOOM TREES AND PARTIAL DATA CUBES},
booktitle={Proceedings of the International Conference on Imaging Theory and Applications and International Conference on Information Visualization Theory and Applications - Volume 1: IVAPP, (VISIGRAPP 2010)},
year={2010},
pages={101-111},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002829301010111},
isbn={978-989-674-027-6},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Imaging Theory and Applications and International Conference on Information Visualization Theory and Applications - Volume 1: IVAPP, (VISIGRAPP 2010)
TI - MULTISCALE VISUALIZATION OF RELATIONAL DATABASES USING LAYERED ZOOM TREES AND PARTIAL DATA CUBES
SN - 978-989-674-027-6
AU - Wang B.
AU - Chen G.
AU - Bu J.
AU - Yu Y.
PY - 2010
SP - 101
EP - 111
DO - 10.5220/0002829301010111