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
- 2. M. Trencséni
- 3. G. Herczegh
- 4. L. Dobos
- 5. P. Józsa
- 6. N. Purger
- 7. T. Budavári
- 8. A. Szalay
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