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: 1947
Venue: PODS
Year: 2024
Pagerank: 5.093636e-05
Overall Rank: 11,124 | 23.68%
DOI: 10.1145/3651147

Incoming Non-self Citations Over Time

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

Authors

1. Peter Lindner (EPFL)
2. Sachin Basil John (EPFL)
3. Christoph Koch (EPFL)
4. Dan Suciu (University of Washington)

BibTeX Citation

@inproceedings{lindner_pods24,
        address = {New York, NY, USA},
        series = {{PODS} '24},
        title = {{The Moments Method for Approximate Data Cube Queries}},
        url = {https://dl.acm.org/doi/10.1145/3651147},
        doi = {10.1145/3651147},
        booktitle = {Proceedings of the {ACM} {SIGMOD} Symposium on {Principles} of {Database} {Systems}},
        publisher = {Association for Computing Machinery},
        author = {Lindner, Peter and John, Sachin Basil and Koch, Christoph and Suciu, Dan},
        year = {2024}
}

Incoming Citations (Sorted by Pagerank)

Showing 0 of 0 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank

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	0.00071822821
213	Approximate Computation of Multidimensional Aggregates of Sparse Data Using Wavelets	1999	SIGMOD	0.00024723025
11,609	High-dimensional Data Cubes	2022	VLDB	5.093636e-05

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