Discovering Top-k Relevant and Diversified Rules
Summary: Top-k relevant and diversified Entity Enhancing Rules (REEs) for data quality; trains a relevance model and four diversity measures to reduce noise. NP-hard problem; practical parallel algorithm with approximation guarantees delivers real-data speedups. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Wenfei Fan (Beihang University; Shenzhen University; University of Edinburgh)
- 2. Ziyan Han (Beihang University)
- 3. Min Xie (Shenzhen University)
- 4. Guangyi Zhang (Shenzhen University)
BibTeX Citation
@inproceedings{fan_sigmod24,
title = {{Discovering Top-k Relevant and Diversified Rules}},
author = {Fan, Wenfei and Han, Ziyan and Xie, Min and Zhang, Guangyi},
series = {{SIGMOD} '24},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3677131},
url = {https://dl.acm.org/doi/10.1145/3677131},
year = {2024}
}Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 11,062 | Efficient Partition-based Approaches for Diversified Top-k Subgraph Matching | 2026 | VLDB | 4.9769913e-05 |
Previous
Page 1 / 1
Next
Outgoing Citations (Sorted by Pagerank)
Showing 29 of 29 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 | 1,535 | Top-k Query Evaluation with Probabilistic Guarantees | 2004 | VLDB |
| 2 | 1,145 | Efficient Diversity-Aware Search | 2011 | SIGMOD |
| 3 | 467 | Discovery of Multiple-Level Association Rules from Large Databases | 1995 | VLDB |
| 4 | 4,652 | Temporal Rules Discovery for Web Data Cleaning | 2016 | VLDB |
| 5 | 3,338 | On the Complexity of Query Result Diversification | 2013 | VLDB |
| 6 | 7,345 | Discovering Association Rules from Big Graphs | 2022 | VLDB |
| 7 | 650 | Discovering Data Quality Rules | 2008 | VLDB |
| 8 | 11,197 | Incremental Rule Discovery in Response to Parameter Updates | 2025 | SIGMOD |
| 9 | 10,346 | Parallel Rule Discovery from Large Datasets by Sampling | 2022 | SIGMOD |
| 10 | 9,616 | Discovering Top-k Rules using Subjective and Objective Criteria | 2023 | SIGMOD |