Indexing for Function Approximation
Summary: Frames high-dimensional function approximation (HFA) in simulations as a storage/retrieval problem, a new class of high-dimensional index workloads. Proposes a Taylor-series based HFA method and analyzes mixed query/update tradeoffs with experiments. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Biswanath Panda
- 2. Mirek Riedewald
- 3. Stephen B. Pope
- 4. Johannes Gehrke
- 5. L. Paul Chew
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 2 | R-Trees: A Dynamic Index Structure For Spatial Searching | 1984 | SIGMOD | 0.0032169493 |
| 6 | The R*-tree: An Efficient and Robust Access Method for Points and Rectangles | 1990 | SIGMOD | 0.0016162015 |
| 24 | The R+-Tree: A Dynamic Index For Multi-Dimensional Objects | 1987 | VLDB | 0.00083378538 |
| 34 | Similarity Search in High Dimensions via Hashing | 1999 | VLDB | 0.00076637636 |
| 79 | A Quantitative Analysis and Performance Study for Similarity-Search Methods in High-Dimensional Spaces | 1998 | VLDB | 0.00056242144 |
| 129 | The X-tree: An Index Structure for High-Dimensional Data | 1996 | VLDB | 0.0004429571 |
| 8,562 | Contorting High Dimensional Data for Efficient Main Memory KNN Processing | 2003 | SIGMOD | 4.4937074e-05 |
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