High-dimensional Data Cubes
Summary: Supports interactive high-dimensional cubes via judicious partial materialization of binary-domain cubes, reconstructing missing entries with statistical or linear-programming methods. Enables all-candidate-dimension inclusion without storage compromise and accelerates hierarchical dice, roll-up, and drill-down. (summarized by gpt-5.6-luna on Jul 24 2026)
Incoming Non-self Citations Over Time
No non-self incoming citations found for this paper in this database.
Authors
- 1. Sachin Basil John (EPFL)
- 2. Christoph Koch (EPFL)
BibTeX Citation
@article{john_vldb22,
title = {{High-dimensional Data Cubes}},
author = {John, Sachin Basil and Koch, Christoph},
journal = {PVLDB},
series = {{VLDB} '22},
volume = {15},
number = {13},
pages = {3828--3840},
doi = {10.14778/3565838.3565839},
url = {https://doi.org/10.14778/3565838.3565839},
year = {2022}
}
Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 11,124 | The Moments Method for Approximate Data Cube Queries | 2024 | PODS | 5.093636e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 19 of 19 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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