Abstract
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.
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Yang, L. (2003). Visualizing Large Relational Datasets by Combining Grand Tour with Footprint Splatting of High Dimensional Data Cubes. In: Kumar, V., Gavrilova, M.L., Tan, C.J.K., L’Ecuyer, P. (eds) Computational Science and Its Applications — ICCSA 2003. ICCSA 2003. Lecture Notes in Computer Science, vol 2667. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44839-X_2
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DOI: https://doi.org/10.1007/3-540-44839-X_2
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