Spatial Indexing of Large Multidimensional Databases
Summary: Scalable spatial indexes for non‑uniform, terabyte‑scale multidimensional data: layered uniform grids, hierarchical binary space partitioning, and sampled flat Voronoi tessellation that adapt to data distribution. Demonstrated on a 5‑D, 270M‑point SDSS dataset, producing speedups for similarity search, classification and simulation–observation comparison and enabling adaptive multiresolution visualization. (summarized by gpt-5-mini on Feb 09 2026)
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Authors
- 1. I. Csabai (Eotvos Lorand University)
- 2. M. Trencséni (Eotvos Lorand University)
- 3. G. Herczegh (Eotvos Lorand University)
- 4. L. Dobos (Eotvos Lorand University)
- 5. P. Józsa (Eotvos Lorand University)
- 6. N. Purger (Eotvos Lorand University)
- 7. T. Budavári (Johns Hopkins University)
- 8. A. Szalay (Johns Hopkins University)
BibTeX Citation
@inproceedings{csabai_cidr07,
address = {Amsterdam, Netherlands},
series = {{CIDR} '07},
title = {{Spatial Indexing of Large Multidimensional Databases}},
booktitle = {Proceedings of the {Conference} on {Innovative} {Data} {Systems} {Research}},
author = {Csabai, I. and Trencséni, M. and Herczegh, G. and Dobos, L. and Józsa, P. and Purger, N. and Budavári, T. and Szalay, A.},
year = {2007}
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