Skip to main content

ZoomTree: Unrestricted Zoom Paths in Multiscale Visual Analysis of Relational Databases

  • Conference paper
Computer Vision, Imaging and Computer Graphics. Theory and Applications (VISIGRAPP 2010)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 229))

  • 1734 Accesses

Abstract

Unrestricted zoom paths are much desired to gain deep understandings during visual analysis of relational databases. We present a multiscale visualization system supporting unrestricted zoom paths. Our system has a flexible visual interface on the client side, called “ZoomTree”, and a powerful and efficient back end with GPU-based parallel online data cubing and CPU-based data clustering. Zoom-trees are seamlessly integrated with a table-based overview using “hyperlinks” embedded in the table, and are designed to represent the entire history of a zooming process that reveals multiscale data characteristics. Arbitrary branching and backtracking in a zoom-tree are made possible by our fast parallel online cubing algorithm for partially materialized data cubes. Partial materialization provides a good tradeoff among preprocessing time, storage and online query time. Experiments and a user study have confirmed the effectiveness of our design.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Gray, J., Bosworth, A., Lyaman, A., Pirahesh, H.: Data Cube: A Relational Aggregation Operator Generalizing Group-By, Cross-Tab and Sub-Totals 1, 29–54 (1996)

    Google Scholar 

  2. Sarawagi, S., Agrawal, R., Megiddo, N.: Discovery-Driven Exploration of OLAP Data Cubes. In: Schek, H.-J., Saltor, F., Ramos, I., Alonso, G. (eds.) EDBT 1998. LNCS, vol. 1377, pp. 168–182. Springer, Heidelberg (1998)

    Chapter  Google Scholar 

  3. Chaudhuri, S., Dayal, U.: An Overview of Data Warehousing and OLAP Technology. SIGMOD Record 26, 65–74 (1997)

    Article  Google Scholar 

  4. Stolte, C., Tang, D., Hanrahan, P.: Polaris: A System for Query, Analysis, and Visualization of Multidimensional Relational Databases. IEEE Trans. on Visualization and Computer Graphics 8, 52–65 (2002)

    Article  Google Scholar 

  5. Stolte, C., Tang, D., Hanrahan, P.: Query, analysis, and visualization of hierarchically structured data using Polaris. In: KDD 2002: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 112–122 (2002)

    Google Scholar 

  6. Stolte, C., Tang, D., Hanrahan, P.: Multiscale Visualization Using Data Cubes. In: INFOVIS 2002: Proceedings of the IEEE Symposium on Information Visualization, pp. 7–14 (2002)

    Google Scholar 

  7. Stolte, C., Tang, D., Hanrahan, P.: Multiscale Visualization Using Data Cubes. IEEE Trans. on Visualization and Computer Graphics 9, 176–187 (2003)

    Article  Google Scholar 

  8. Shalom, S.A., Dash, M., Tue, M.: Efficient K-Means clustering using accelerated graphics processors. In: Song, I.-Y., Eder, J., Nguyen, T.M. (eds.) DaWaK 2008. LNCS, vol. 5182, pp. 166–175. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  9. Inc., Beezix: Microsoft Excel 2007 Charts and Tables Quick Reference Guide (2007)

    Google Scholar 

  10. Bederson, B.B., Hollan, J.D.: Pad++: a zooming graphical interface for exploring alternate interface physics. In: UIST 1994: ACM Symposium on User Interface Software and Technology, pp. 17–26 (1994)

    Google Scholar 

  11. Mansmann, S., Scholl, M.H.: Exploring OLAP aggregates with hierarchical visualization techniques. In: SAC 2007: ACM Symposium on Applied Computing, pp. 1067–1073 (2007)

    Google Scholar 

  12. Fua, Y.-H., Ward, M.O., Rundensteiner, E.A.: Hierarchical parallel coordinates for exploration of large datasets. In: IEEE Conference on Visualization 1999, pp. 43–50 (1999)

    Google Scholar 

  13. Kreuseler, M., Schumann, H.: Information visualization using a new focus+context technique in combination with dynamic clustering of information space. In: NPIVM 1999: The 1999 Workshop on New Paradigms in Information Visualization and Manipulation, pp. 1–5 (1999)

    Google Scholar 

  14. Inselberg, A., Dimsdale, B.: Parallel coordinates: a tool for visualizing multi-dimensional geometry. In: 1st IEEE Conference on Visualization 1990, pp. 361–378 (1990)

    Google Scholar 

  15. Vinnik, S., Mansmann, F.: From analysis to interactive exploration: Building visual hierarchies from OLAP cubes. In: Ioannidis, Y., Scholl, M.H., Schmidt, J.W., Matthes, F., Hatzopoulos, M., Böhm, K., Kemper, A., Grust, T., Böhm, C. (eds.) EDBT 2006. LNCS, vol. 3896, pp. 496–514. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  16. Antis, J.M., Eick, S.G., Pyrce, J.D.: Visualizing the structure of large relational databases. IEEE Software 13, 72–79 (1996)

    Article  Google Scholar 

  17. Xu, W., Gaither, K.P.: On Interactive Visualization with Relational Database, In: InfoVis 2008, Poster (2008)

    Google Scholar 

  18. Kadivar, N., Chen, V., Dunsmuir, D., Lee, E., Qian, C., Dill, J., Shaw, C., Woodbury, R.: Capturing and supporting the analysis process. In: IEEE Symposium on Visual Analytics Science and Technology, VAST 2009, pp. 131–138 (2009)

    Google Scholar 

  19. Maniatis, A.S., Vassiliadis, P., Skiadopoulos, S., Vassiliou, Y.: Advanced visualization for OLAP. In: DOLAP 2003: 6th ACM International Workshop on Data Warehousing and OLAP, pp. 9–16 (2003)

    Google Scholar 

  20. Rao, R., Card, S.K.: The table lens: merging graphical and symbolic representations in an interactive focus + context visualization for tabular information. In: CHI 1994: SIGCHI Conference on Human Factors in Computing Systems, pp. 318–322 (1994)

    Google Scholar 

  21. Kesaraporn, T., Amitava, D., Robyn, O.: HDDV: Hierarchical Dynamic Dimensional visualization for Multidimensional Data. In: IASTED 2004: International Conference on Databases and Applications, pp. 157–162 (2004)

    Google Scholar 

  22. Techapichetvanich, K., Datta, A.: Interactive visualization for OLAP. In: Gervasi, O., Gavrilova, M.L., Kumar, V., Laganá, A., Lee, H.P., Mun, Y., Taniar, D., Tan, C.J.K. (eds.) ICCSA 2005 Part III. LNCS, vol. 3482, pp. 206–214. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  23. Proclarity Analytics (June 2006), http://www.proclarity.com/products/proclarity_analytics_6.asp

  24. Report Portal 2006: Zero-footprint olap web client solution XMLA consluting (2006), http://www.reportportal.com

  25. Rundensteiner, E.A., Ward, M.O., Yang, J., Doshi, P.R.: XmdvTool: visual interactive data exploration and trend discovery of high-dimensional data sets. In: SIGMOD 2002: 2002 ACM SIGMOD International Conference on Management of Data, pp. 631–631 (2002)

    Google Scholar 

  26. Keim, D.A., Kriegel, H.-P., Ankerst, M.: Recursive pattern: a technique for visualizing very large amountsof data. In: Proc.1995 IEEE Conference on Visualization, pp. 279–286 (1995)

    Google Scholar 

  27. Allison, W., Chris, O., Alexander, A., Michael, C., Vuk, E., Mark, L., Mybrid, S., Michael, S.: DataSplash: A Direct Manipulation Environment for Programming Semantic Zoom Visualizations of Tabular Data. Journal of Visual Languages & Computing 12, 551–571 (2001)

    Article  Google Scholar 

  28. Peng, W., Ward, M.O., Rundensteiner, E.A.: Clutter Reduction in Multi-Dimensional Data Visualization Using Dimension Reordering. In: INFOVIS 2004: Proceedings of the IEEE Symposium on Information Visualization, pp. 89–96 (2004)

    Google Scholar 

  29. Ellis, G., Dix, A.: A Taxonomy of Clutter Reduction for Information Visualisation. IEEE Transactions on Visualization and Computer Graphics 13, 1216–1223 (2007)

    Article  Google Scholar 

  30. Han, J., Chen, Y., Dong, G., Pei, J., Wah, B.W., Wang, J., Cai, Y.D.: Stream Cube: An Architecture for Multi-Dimensional Analysis of Data Streams. Distributed and Parallel Databases 18, 173–197 (2005)

    Article  Google Scholar 

  31. Han, J., Pei, J., Dong, G., Wang, K.: Efficient Computation of Iceberg Cubes with Complex Measures. In: SIGMOD 2001 (2001)

    Google Scholar 

  32. Harris, M., Owens, J.D., Sengupta, S., Zhang, Y., Davidson, A.: CUDPP library (2007)

    Google Scholar 

  33. Lu, H., Huang, X., Li, Z.: Computing data cubes using massively parallel processors. In: Proc. 7th Parallel Computing Workshop (1997)

    Google Scholar 

  34. Dehne, F., Eavis, T., Hambrusch, S., Rau-Chaplin, A.: Parallelizing the Data Cube. Distributed and Parallel Databases 11, 181–201 (2002)

    MATH  Google Scholar 

  35. Dehne, F., Eavis, T., Rau-Chaplin, A.: Computing Partial Data Cubes for Parallel Data Warehousing Applications. In: Cotronis, Y., Dongarra, J. (eds.) PVM/MPI 2001. LNCS, vol. 2131, pp. 319–326. Springer, Heidelberg (2001)

    Chapter  Google Scholar 

  36. Amdrews, D.F.: Plots of high-dimensional data. Biometrics 29, 125–136 (1972)

    Article  Google Scholar 

  37. NVidia CUDA Programming Guide 2.0 (2008)

    Google Scholar 

  38. Harris, M.: Optimizing Parallel Reduction in CUDA (2008)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2011 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Wang, B., Chen, G., Bu, J., Yu, Y. (2011). ZoomTree: Unrestricted Zoom Paths in Multiscale Visual Analysis of Relational Databases. In: Richard, P., Braz, J. (eds) Computer Vision, Imaging and Computer Graphics. Theory and Applications. VISIGRAPP 2010. Communications in Computer and Information Science, vol 229. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25382-9_21

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-25382-9_21

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-25381-2

  • Online ISBN: 978-3-642-25382-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics