Rock: Cleaning Data by Embedding ML in Logic Rules
Summary: Rock unifies ML and logic by embedding classifiers as predicates in rules for entity resolution, conflict resolution, timeliness, and imputation. Batch/incremental rule learning, error detection, and corrections from rules and ground truth. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Xianchun Bao (Grandhoo Inc.; Shenzhen University)
- 2. Zian Bao (Grandhoo Inc.; Shenzhen University)
- 3. Binbin Bie (Grandhoo Inc.; Shenzhen University)
- 4. QingSong Duan (Grandhoo Inc.; Shenzhen University)
- 5. Wenfei Fan (Beihang University; Shenzhen University; University of Edinburgh)
- 6. Hui Lei (Grandhoo Inc.; Shenzhen University)
- 7. Daji Li (Grandhoo Inc.; Shenzhen University)
- 8. Wei Lin (Grandhoo Inc.; Shenzhen University)
- 9. Peng Liu (Grandhoo Inc.; Shenzhen University)
- 10. Zhicong Lv (Grandhoo Inc.; Shenzhen University)
- 11. Mingliang Ouyang (Grandhoo Inc.; Shenzhen University)
- 12. Shuai Tang (Grandhoo Inc.; Shenzhen University)
- 13. Yaoshu Wang (Grandhoo Inc.; Shenzhen University)
- 14. Qiyuan Wei (Grandhoo Inc.; Shenzhen University)
- 15. Min Xie (Grandhoo Inc.; Shenzhen University)
- 16. Jing Zhang (Grandhoo Inc.; Shenzhen University)
- 17. Xin Zhang (Grandhoo Inc.; Shenzhen University)
- 18. Runxiao Zhao (Grandhoo Inc.; Shenzhen University)
- 19. Shuping Zhou (Grandhoo Inc.; Shenzhen University)
BibTeX Citation
@inproceedings{bao_sigmod24,
title = {{Rock: Cleaning Data by Embedding ML in Logic Rules}},
author = {Bao, Xianchun and Bao, Zian and Bie, Binbin and Duan, QingSong and Fan, Wenfei and Lei, Hui and Li, Daji and Lin, Wei and Liu, Peng and Lv, Zhicong and Ouyang, Mingliang and Tang, Shuai and Wang, Yaoshu and Wei, Qiyuan and Xie, Min and Zhang, Jing and Zhang, Xin and Zhao, Runxiao and Zhou, Shuping},
series = {{SIGMOD} '24},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3626246.3653372},
url = {https://dl.acm.org/doi/10.1145/3626246.3653372},
year = {2024}
}Incoming Citations (Sorted by Pagerank)
Showing 8 of 8 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 8,990 | nsDB: Architecting the Next Generation Database by Integrating Neural and Symbolic Systems | 2024 | VLDB | 5.2418665e-05 |
| 9,467 | HyperBlocker: Accelerating Rule-based Blocking in Entity Resolution using GPUs | 2025 | VLDB | 5.1709969e-05 |
| 10,189 | Discovering Top-k Relevant and Diversified Rules | 2024 | SIGMOD | 5.0628015e-05 |
| 10,541 | Outliers: The Good, the Bad and the Ugly | 2026 | SIGMOD | 4.9769913e-05 |
| 10,990 | NiceT: Named Entity Cleaning and Enhancement with Human-in-the-loop | 2026 | VLDB | 4.9769913e-05 |
| 11,191 | Data Enhancement for Binary Classification of Relational Data | 2025 | SIGMOD | 4.9769913e-05 |
| 11,197 | Incremental Rule Discovery in Response to Parameter Updates | 2025 | SIGMOD | 4.9769913e-05 |
| 11,641 | Rock: Cleaning Data with both ML and Logic Rules | 2024 | VLDB | 4.9769913e-05 |
Previous
Page 1 / 1
Next
Outgoing Citations (Sorted by Pagerank)
Showing 44 of 44 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 | 104 | HoloClean: Holistic Data Repairs with Probabilistic Inference | 2017 | VLDB |
| 2 | 1,044 | Data Cleaning: Overview and Emerging Challenges | 2016 | SIGMOD |
| 3 | 4,652 | Temporal Rules Discovery for Web Data Cleaning | 2016 | VLDB |
| 4 | 10,538 | In-context Clustering-based Entity Resolution with Large Language Models: A Design Space Exploration | 2026 | SIGMOD |
| 5 | 11,744 | Splitting Tuples of Mismatched Entities | 2023 | SIGMOD |
| 6 | 2,967 | Towards Dependable Data Repairing with Fixing Rules | 2014 | SIGMOD |
| 7 | 9,626 | Interactive and Deterministic Data Cleaning: A Tossed Stone Raises a Thousand Ripples | 2016 | SIGMOD |
| 8 | 7,880 | Learning Over Dirty Data Without Cleaning | 2020 | SIGMOD |
| 9 | 9,813 | Making It Tractable to Catch Duplicates and Conflicts in Graphs | 2023 | SIGMOD |
| 10 | 11,641 | Rock: Cleaning Data with both ML and Logic Rules | 2024 | VLDB |