G-CARE: A Framework for Performance Benchmarking of Cardinality Estimation Techniques for Subgraph Matching
Summary: Introduces G-CARE, a unified framework to realize and benchmark all existing cardinality-estimation techniques for subgraph matching on graph and relational DBs. Evaluation on RDF and non-RDF graphs shows widespread inaccuracies in prior methods; a simple online-aggregation sampling approach consistently outperforms them. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Yeonsu Park (Pohang University of Science and Technology)
- 2. Seongyun Ko (Pohang University of Science and Technology)
- 3. Sourav S Bhowmick (Nanyang Technological University)
- 4. Kyoungmin Kim (Pohang University of Science and Technology)
- 5. Kijae Hong (Pohang University of Science and Technology)
- 6. Wook-Shin Han (Pohang University of Science and Technology)
BibTeX Citation
@inproceedings{park_sigmod20,
title = {{G-CARE: A Framework for Performance Benchmarking of Cardinality Estimation Techniques for Subgraph Matching}},
author = {Park, Yeonsu and Ko, Seongyun and Bhowmick, Sourav S and Kim, Kyoungmin and Hong, Kijae and Han, Wook-Shin},
series = {{SIGMOD} '20},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3318464.3389702},
url = {https://dl.acm.org/doi/10.1145/3318464.3389702},
year = {2020}
}
Incoming Citations (Sorted by Pagerank)
Showing 24 of 24 citing papers.
Previous
Page 1 / 1
Next
Outgoing Citations (Sorted by Pagerank)
Showing 27 of 27 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
Previous
Page 1 / 1
Next
Semantically Similar Papers
| # | Overall Rank | Paper | Year | Venue |
|---|---|---|---|---|
| 1 | 10,564 | gMatch: Fine-Grained and Hardware-Efficient Subgraph Matching on GPUs | 2026 | VLDB |
| 2 | 10,027 | Color: A Framework for Applying Graph Coloring to Subgraph Cardinality Estimation | 2025 | VLDB |
| 3 | 5,942 | Cardinality Estimation of Subgraph Matching: A Filtering-Sampling Approach | 2024 | VLDB |
| 4 | 5,712 | Sample-Efficient Cardinality Estimation Using Geometric Deep Learning | 2024 | VLDB |
| 5 | 3,070 | Accurate Summary-based Cardinality Estimation Through the Lens of Cardinality Estimation Graphs | 2022 | VLDB |
| 6 | 9,996 | Path-centric Cardinality Estimation for Subgraph Matching | 2025 | VLDB |
| 7 | 10,875 | Data-Agnostic Cardinality Learning from Imperfect Workloads | 2025 | VLDB |
| 8 | 1,122 | Cardinality Estimation in DBMS: A Comprehensive Benchmark Evaluation | 2022 | VLDB |
| 9 | 6,341 | Cardinality Estimation over Knowledge Graphs with Embeddings and Graph Neural Networks | 2024 | SIGMOD |
| 10 | 1,774 | gStore: Answering SPARQL Queries via Subgraph Matching | 2011 | VLDB |