Cardinality Estimation is a query optimization technique that belongs to the class of database performance modeling methods, specifically distinguished by its purpose of predicting the number of distinct elements or tuples that satisfy a given query condition or data access pattern, often employing sublinear algorithms to achieve scalable and efficient approximations.
505
Publications
25.4K
Citations
1.1K
Authors
479
Institutions
Publications per year
2017–2026
171
Publications
505
How good are query optimizers, really?
Viktor Leis, Andrey Gubichev, Atanas Mirchev et al. · Proceedings of the VLDB Endowment · 2015 · 673 citations · Full text
1–5 of 505
Authors
1.1K
- SC
9
h-index
- VM
9
h-index
- SC
9
h-index
- JF
9
h-index
- ML
6
h-index
1–5 of 1.1K
Institutions
479
Tsinghua University
Beijing, China
12 h-index
34 publications
2.7K citations
University of Wisconsin–Madison
Madison, United States
12 h-index
23 publications
2.6K citations
Microsoft (United States)
Redmond, United States
12 h-index
23 publications
1.4K citations
University of Florida
Gainesville, United States
11 h-index
30 publications
922 citations
Nanyang Technological University
Singapore, Singapore
11 h-index
31 publications
1.7K citations
University of California, Berkeley
Berkeley, United States
10 h-index
27 publications
2.9K citations
University of Washington
Seattle, United States
8 h-index
14 publications
853 citations
Xidian University
Xi'an, China
8 h-index
21 publications
468 citations
1–8 of 479
Venues
- Proceedings of the VLDB Endowment
PVLDB · 35 publications · 3K citations
28
h-index
- arXiv (Cornell University)
Repository · 24 publications · 649 citations
16
h-index
- IEEE Transactions on Information Theory
15 publications · 413 citations
12
h-index
- IEEE/ACM Transactions on Networking
TON · 8 publications · 302 citations
8
h-index
- ACM Transactions on Database Systems
TODS · 6 publications · 668 citations
6
h-index
1–5