XmdvTool: Visual Interactive Data Exploration and Trend Discovery of High-dimensional Data Sets

Summary: XmdvTool enables interactive visual exploration of high-dimensional data through tightly linked multi-view displays and brushing across screen, data, and structure spaces. It uniquely combines hierarchical navigation with MinMax-trees and dimension-cluster trees, plus prefetching and semantic caching for responsive, scalable HD analysis. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID: 3468
Venue: SIGMOD
Year: 2002
Pagerank: -
Overall Rank: 13,967 | 4.18%
DOI: 10.1145/564691.564786

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Authors

1. Elke A. Rundensteiner (Worcester Polytechnic Institute)
2. Matthew O. Ward (Worcester Polytechnic Institute)
3. Jing Yang (Worcester Polytechnic Institute)
4. Punit R. Doshi (Worcester Polytechnic Institute)

BibTeX Citation

@inproceedings{rundensteiner_sigmod02,
        title = {{XmdvTool: Visual Interactive Data Exploration and Trend Discovery of High-dimensional Data Sets}},
        author = {Rundensteiner, Elke A. and Ward, Matthew O. and Yang, Jing and Doshi, Punit R.},
        series = {{SIGMOD} '02},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/564691.564786},
        url = {https://dl.acm.org/doi/10.1145/564691.564786},
        year = {2002}
}

Incoming Citations (Sorted by Pagerank)

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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

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