Database Paper Browser

Back to papers

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)

Paper ID
1915
Venue
PODS
Year
2024
Pagerank
4.1945683e-05
Overall Rank
10,902 | 24.16%
DOI
10.1145/3651147

Incoming Non-self Citations Over Time

No non-self incoming citations found for this paper in this database.

Authors

Incoming Citations (Sorted by Pagerank)

Showing 0 of 0 citing papers.

Rank Citing Paper Year Venue Pagerank
Previous Page 1 / 1 Next

Outgoing Citations (Sorted by Pagerank)

Showing 3 of 3 cited papers.

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
Previous Page 1 / 1 Next

Semantically Similar Papers