The grand tour: a tool for viewing multidimensional data
SIAM Journal on Scientific and Statistical Computing
Algorithms for clustering data
Algorithms for clustering data
Analyzing high-dimensional data with motion graphics
SIAM Journal on Scientific and Statistical Computing
Footprint evaluation for volume rendering
SIGGRAPH '90 Proceedings of the 17th annual conference on Computer graphics and interactive techniques
Hypervolume visualization: a challenge in simplicity
VVS '98 Proceedings of the 1998 IEEE symposium on Volume visualization
ACM Computing Surveys (CSUR)
Interactive exploration of very large relational datasets through 3D dynamic projections
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
High Dimensional Brushing for Interactive Exploration of Multivariate Data
VIS '95 Proceedings of the 6th conference on Visualization '95
Research Report: Volume Rendering for Relational Data
INFOVIS '97 Proceedings of the 1997 IEEE Symposium on Information Visualization (InfoVis '97)
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Large relational datasets have always been a challenge for information visualization due to their high dimensionalities and their large sizes. One approach to this challenge is to combine grand tour and volume rendering with the support of data aggregation from databases to deal with both high dimensionality of data and large number of relational records. This paper focuses on how to efficiently produce explanatory images that give comprehensive insights into the global data distribution features, such as data clusters and holes, in large relation datasets. Multidimensional footprint splatting is implemented to directly render relational data. Footprint splatting is implemented by using texture mapping accelerated by graphics hardware. Experiments have shown the usefulness of the approach to display data clusters and to identify interesting patterns in high dimensional relational datasets.