Rudolf: Interactive Rule Refinement System for Fraud Detection

Summary: RUDOLF interactively refines expert-written fraud-detection rules as transaction patterns evolve. It automatically proposes candidate adaptations separating fraudulent from legitimate events, while preserving expert control; demonstrated for credit-card fraud and network attacks. (summarized by gpt-5.6-luna on Jul 24 2026)

Paper ID: 11446
Venue: VLDB
Year: 2016
Pagerank: 6.2989009e-05
Overall Rank: 5,253 | 63.97%
DOI: 10.14778/3007263.3007270

Incoming Non-self Citations Over Time

Authors

1. Tova Milo (Tel Aviv University)
2. Slava Novgorodov (Tel Aviv University)
3. Wang-Chiew Tan (University of California Santa Cruz)

BibTeX Citation

@article{milo_vldb16,
        title = {{Rudolf: Interactive Rule Refinement System for Fraud Detection}},
        author = {Milo, Tova and Novgorodov, Slava and Tan, Wang-Chiew},
        journal = {PVLDB},
        series = {{VLDB} '16},
        volume = {9},
        number = {13},
        pages = {1465--1468},
        doi = {10.14778/3007263.3007270},
        url = {https://doi.org/10.14778/3007263.3007270},
        year = {2016}
}

Incoming Citations (Sorted by Pagerank)

Showing 3 of 3 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
8,160	ICARUS: Minimizing Human Effort in Iterative Data Completion	2018	VLDB	5.4754476e-05
11,465	MINT: Detecting Fraudulent Behaviors from Time-series Relational Data	2023	VLDB	5.093636e-05
11,492	Fanglue: An Interactive System for Decision Rule Crafting	2023	VLDB	5.093636e-05

Outgoing Citations (Sorted by Pagerank)

Showing 2 of 2 cited papers.

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

Rank	Cited Paper	Year	Venue	Pagerank
385	Why Not?	2009	SIGMOD	0.00019455743
4,076	A Probabilistic Optimization Framework for the Empty-Answer Problem	2013	VLDB	6.9221203e-05

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