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: h83acd4a53822e054
Venue: SIGMOD
Year: 2021
Pagerank: 5.6529909e-05
Overall Rank: 6,899 | 53.62%
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
10,063	Fast Search-By-Classification for Large-Scale Databases Using Index-Aware Decision Trees and Random Forests	2023	VLDB	5.0875952e-05
11,196	Subgroup Discovery with Small and Alternative Feature Sets	2025	SIGMOD	4.9793485e-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	0.00064420972
164	Mining Frequent Patterns without Candidate Generation	2000	SIGMOD	0.00027412227
620	Mining Quantitative Association Rules in Large Relational Tables	1996	SIGMOD	0.00015502611
721	Dynamic Itemset Counting and Implication Rules for Market Basket Data	1997	SIGMOD	0.0001452081
878	Interpretable and Informative Explanations of Outcomes	2015	VLDB	0.00013302631

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