From Discrepancy to Declustering: Near-optimal multidimensional declustering strategies for range queries [Extended Abstract]
Summary: Maps low-discrepancy point sets to multidimensional declustering to achieve near-optimal additive error O(log^{d-1} M) for range queries, improving prior O(M^{d-1}) bounds for d≥3. Two constructions: one for M a prime-power ≥d; another needs data size polynomial in M but removes the M restriction. (summarized by gpt-5-mini on Feb 09 2026)
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| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 1,701 | Optimal File Distribution For Partial Match Retrieval | 1988 | SIGMOD | 0.00010856554 |
| 3,222 | The Idea of De-Clustering and Its Applications | 1986 | VLDB | 7.3506864e-05 |
| 4,740 | CMD: A Multidimensional Declustering Method for Parallel Database Systems | 1992 | VLDB | 5.9594889e-05 |
| 6,516 | (Almost) Optimal Parallel Block Access for Range Queries | 2000 | PODS | 5.0321577e-05 |
| 7,974 | Declustering Objects for Visualization | 1993 | VLDB | 4.613363e-05 |
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