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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
h4de88e4a3c151181
Venue
VLDB
Year
2016
Pagerank
6.1633336e-05
Overall Rank
5,354 | 64.01%
DOI
10.14778/3007263.3007270

Incoming Non-self Citations Over Time

Authors

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
8,335 ICARUS: Minimizing Human Effort in Iterative Data Completion 2018 VLDB 5.3525933e-05
9,876 MINT: Detecting Fraudulent Behaviors from Time-series Relational Data 2023 VLDB 5.1176637e-05
11,801 Fanglue: An Interactive System for Decision Rule Crafting 2023 VLDB 4.9793485e-05
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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
391 Why Not? 2009 SIGMOD 0.00019238698
4,165 A Probabilistic Optimization Framework for the Empty-Answer Problem 2013 VLDB 6.7675283e-05
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