zoom interfaces for spatial dimensions.
ACKNOWLEDGEMENTS
We would like to thank Shaowen Wang for the his-
torical climate dataset, Jiawei Han and Pat Han-
rahan for helpful discussions and suggestions, and
the anonymous reviewers for valuable comments.
This work was partially supported by NSF (IIS
09-14631), National Natural Science Foundation of
China (60728204/F020404).
REFERENCES
Proclarity analytics 6 2006. from: http://www.proclarity.
com/products/proclarity analytics 6.asp.
Report portal 2006:zero-footprint olap web client solution
xmla consluting. from:. http://www.reportportal.com.
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 tabu-
lar data. Journal of Visual Languages & Computing,
12:551–571.
Antis, J., Eick, S., and Pyrce, J. (1996). Visualizing
the structure of large relational databases. Software,
IEEE, 13(1):72–79.
Bederson, B. B. and Hollan, J. D. (1994). Pad++: a zoom-
ing graphical interface for exploring alternate inter-
face physics. In UIST ’94: ACM symposium on user
interface software and technology.
Chaudhuri, S. and Dayal, U. (1997). An overview of
data warehousing and OLAP technology. SIGMOD
Record, 26:65–74.
CUDA (2008). Nvidia cuda (compute unified de-
vice architecture) programming guide 2.0.
http://developer.nvidia.com/object/cuda.html.
Ellis, G. and Dix, A. (2007). A taxonomy of clutter reduc-
tion for information visualisation. IEEE Transactions
on Visualization and Computer Graphics, 13(6).
Fua, Y.-H., Ward, M. O., and Rundensteiner, E. A. (1999).
Hierarchical parallel coordinates for exploration of
large datasets. In IEEE conference on Visualization
’99.
Gray, J., Chaudhuri, S., Bosworth, A., Layman, A., Re-
ichart, 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.
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. Dis-
tributed and Parallel Databases, 18(2):173–197.
Han, J., Pei, J., Dong, G., and Wang, K. (2001). Efficient
computation of iceberg cubes with complex measures.
In SIGMOD.
Harris, M. (2008). Optimizing parallel reduction in cuda.
http://developer.download.nvidia.com/compute/cuda
/sdk/website/projects/reduction/doc/reduction.pdf.
Inc, B. (2007). Microsoft Excel 2007 Charts & Tables Quick
Reference Guide.
Kesaraporn, T., Amitava, D., and Robyn, O. (2004). Hddv:
Hierarchical dynamic dimensional visualization for
multidimensional data. In IASTED ’2004: Inter-
national Conference on Databases and Applications,
pages 157–162.
Kreuseler, M. and Schumann, H. (1999). Information vi-
sualization using a new focus+context technique in
combination with dynamic clustering of information
space. In NPIVM ’99: the 1999 workshop on new
paradigms in information visualization and manipu-
lation.
Maniatis, A. S., Vassiliadis, P., Skiadopoulos, S., and Vas-
siliou, Y. (2003). Advanced visualization for olap.
In DOLAP ’03: 6th ACM international workshop on
Data warehousing and OLAP.
Mansmann, S. and Scholl, M. H. (2007). Exploring olap
aggregates with hierarchical visualization techniques.
In SAC ’07: ACM symposium on Applied computing.
Peng, W., Ward, M. O., and Rundensteiner, E. A. (2004).
Clutter reduction in multi-dimensional data visualiza-
tion using dimension reordering. In INFOVIS ’04:
Proceedings of the IEEE Symposium on Information
Visualization.
Rao, R. and Card, S. K. (1994). The table lens: merging
graphical and symbolic representations in an interac-
tive focus + context visualization for tabular informa-
tion. In CHI ’94: SIGCHI conference on Human fac-
tors in computing systems.
Rundensteiner, E. A., Ward, M. O., Yang, J., and Doshi,
P. R. (2002). Xmdvtool: visual interactive data explo-
ration and trend discovery of high-dimensional data
sets. In SIGMOD ’02: 2002 ACM SIGMOD interna-
tional conference on Management of data.
Shalom, S. A., Dash, M., and Tue, M. (2008). Efficient
k-means clustering using accelerated graphics proces-
sors. In DaWaK ’08: 10th international conference on
Data Warehousing and Knowledge Discovery.
Stolte, C., Tang, D., and Hanrahan, P. (2002). Polaris: A
system for query, analysis, and visualization of multi-
dimensional relational databases. IEEE Trans. on Vi-
sualization and Computer Graphics, 8:52–65.
Stolte, C., Tang, D., and Hanrahan, P. (2003). Multiscale
visualization using data cubes. IEEE Trans. on Visu-
alization and Computer Graphics, 9:176–187.
Techapichetvanich, K. and Datta, A. (2005). Interactive vi-
sualization for olap. In ICCSA ’2005: International
Conference on Computational Science and its Appli-
cations Part III, pages 206–214.
Weijia Xu, K. P. (2008). On interactive visualization with
relational database. In InfoVis’2008, Poster.
MULTISCALE VISUALIZATION OF RELATIONAL DATABASES USING LAYERED ZOOM TREES AND
PARTIAL DATA CUBES
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