Dynamic Multidimensional Histograms
Summary: Dynamic multidimensional histograms for continuous streams; a compact dynamic summary tracks distribution and yields on-demand accurate histograms. Algorithms give approximate guarantees on histogram quality; evaluation on real data validates tradeoffs. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Nitin Thaper
- 2. Sudipto Guha
- 3. Piotr Indyk
- 4. Nick Koudas
Incoming Citations (Sorted by Pagerank)
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Showing 19 of 19 cited papers.
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| 114 | Equi-Depth Histograms For Estimating Selectivity Factors For Multi-Dimensional Queries | 1988 | SIGMOD | 0.00046317654 |
| 5,832 | Fast and Near–Optimal Algorithms for Approximating Distributions by Histograms | 2015 | PODS | 5.3085972e-05 |
| 11,437 | Data-Independent Space Partitionings for Summaries | 2021 | PODS | 4.1905499e-05 |