REDS: Rule Extraction for Discovering Scenarios

Summary: REDS introduces rule extraction for scenario discovery by bootstrapping subgroup discovery with an intermediate ML labeler on few simulations. It reduces simulations by 50–75%, enables semi-supervised discovery, and improves scenario quality on third-party data when a simulator is unavailable. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID: 6253
Venue: SIGMOD
Year: 2021
Pagerank: 5.7827401e-05
Overall Rank: 6,759 | 53.63%
DOI: 10.1145/3448016.3457301

Incoming Non-self Citations Over Time

Authors

1. Vadim Arzamasov (Karlsruhe Institute of Technology)
2. Klemens Böhm (Karlsruhe Institute of Technology)

BibTeX Citation

@inproceedings{arzamasov_sigmod21,
        title = {{REDS: Rule Extraction for Discovering Scenarios}},
        author = {Arzamasov, Vadim and Böhm, Klemens},
        series = {{SIGMOD} '21},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3448016.3457301},
        url = {https://dl.acm.org/doi/10.1145/3448016.3457301},
        year = {2021}
}

Incoming Citations (Sorted by Pagerank)

Showing 2 of 2 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
9,874	Fast Search-By-Classification for Large-Scale Databases Using Index-Aware Decision Trees and Random Forests	2023	VLDB	5.2043672e-05
10,778	Subgroup Discovery with Small and Alternative Feature Sets	2025	SIGMOD	5.093636e-05

Outgoing Citations (Sorted by Pagerank)

Showing 5 of 5 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank	Cited Paper	Year	Venue	Pagerank
13	Mining Association Rules between Sets of Items in Large Databases	1993	SIGMOD	0.0006567919
161	Mining Frequent Patterns without Candidate Generation	2000	SIGMOD	0.00027981772
606	Mining Quantitative Association Rules in Large Relational Tables	1996	SIGMOD	0.00015804851
704	Dynamic Itemset Counting and Implication Rules for Market Basket Data	1997	SIGMOD	0.00014837704
858	Interpretable and Informative Explanations of Outcomes	2015	VLDB	0.0001356511

Semantically Similar Papers

#	Overall Rank	Paper	Year	Venue
1	6,689	Ratio Rules: A New Paradigm for Fast, Quantifiable Data Mining	1998	VLDB
2	11,100	RED: Effective Trajectory Representation Learning with Comprehensive Information	2025	VLDB
3	10,766	Incremental Rule Discovery in Response to Parameter Updates	2025	SIGMOD
4	9,999	Discovering Top-k Relevant and Diversified Rules	2024	SIGMOD
5	11,249	Efficient Discovery of Significant Patterns with Few-Shot Resampling	2024	VLDB
6	9,313	Computing Rule-Based Explanations by Leveraging Counterfactuals	2023	VLDB
7	10,778	Subgroup Discovery with Small and Alternative Feature Sets	2025	SIGMOD
8	7,330	Discovering Association Rules from Big Graphs	2022	VLDB
9	9,427	Discovering Top-k Rules using Subjective and Objective Criteria	2023	SIGMOD
10	10,111	Parallel Rule Discovery from Large Datasets by Sampling	2022	SIGMOD