The Moments Method for Approximate Data Cube Queries
Summary: Treats data cubes as probability distributions and approximates aggregate queries via a series expansion (Moments/Bahadur/Fourier), truncating unknown terms to zero to produce estimates. Develops worst-case error bounds, workload-optimal materialization, a new heuristic, and monotonicity guarantees. (summarized by gpt-5-mini on Feb 09 2026)
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Authors
- 1. Peter Lindner
- 2. Sachin Basil John
- 3. Christoph Koch
- 4. Dan Suciu
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Outgoing Citations (Sorted by Pagerank)
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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 |
|---|---|---|---|---|
| 11 | Implementing Data Cubes Efficiently | 1996 | SIGMOD | 0.0011708144 |
| 273 | Approximate Computation of Multidimensional Aggregates of Sparse Data Using Wavelets | 1999 | SIGMOD | 0.00029390945 |
| 11,411 | High-dimensional Data Cubes | 2022 | VLDB | 4.1945683e-05 |
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