Modeling High-Dimensional Index Structures using Sampling
Summary: Proposes a sampling-based cost model to predict index-page accesses for high-dimensional structures—dimensionality-agnostic and able to preserve data clusters. Provides a general page-layout estimator with error compensation and a memory-efficient implementation that yields fast predictions, up to 100x faster than building/probing the full index. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
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Incoming Citations (Sorted by Pagerank)
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| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 3,213 | Predicting Cost Amortization for Query Services | 2011 | SIGMOD | 7.3749055e-05 |
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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| 8,602 | Structure-Aware Sampling: Flexible and Accurate Summarization | 2011 | VLDB | 4.4822166e-05 |
| 9,351 | Can Learned Indexes be Built Efficiently? A Deep Dive into Sampling Trade-offs | 2024 | SIGMOD | 4.3490308e-05 |
| 9,760 | Practical Dynamic Extension for Sampling Indexes | 2023 | SIGMOD | 4.2838028e-05 |