GOLDRUSH: Rule Sharing System for Fraud Detection

Summary: GOLDRUSH enables cross-context reuse of expert-authored fraud rules by abstracting condition semantics and adapting them to new domains. Cost-benefit optimization identifies effective adaptations, supporting collaborative rule engineering alongside ML. (summarized by gpt-5.6-luna on Jul 24 2026)

Paper ID: 11882
Venue: VLDB
Year: 2018
Pagerank: 5.3058708e-05
Overall Rank: 9,205 | 36.85%
DOI: 10.14778/3229863.3236244

Incoming Non-self Citations Over Time

Authors

1. Ariel Jarovsky (Tel Aviv University)
2. Tova Milo (Tel Aviv University)
3. Slava Novgorodov (Tel Aviv University)
4. Wang-Chiew Tan (Megagon Labs)

BibTeX Citation

@article{jarovsky_vldb18,
        title = {{GOLDRUSH: Rule Sharing System for Fraud Detection}},
        author = {Jarovsky, Ariel and Milo, Tova and Novgorodov, Slava and Tan, Wang-Chiew},
        journal = {PVLDB},
        series = {{VLDB} '18},
        volume = {11},
        number = {12},
        pages = {1998--2001},
        doi = {10.14778/3229863.3236244},
        url = {https://doi.org/10.14778/3229863.3236244},
        year = {2018}
}

Incoming Citations (Sorted by Pagerank)

Showing 2 of 2 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
11,465	MINT: Detecting Fraudulent Behaviors from Time-series Relational Data	2023	VLDB	5.093636e-05
11,790	Minimization of Classifier Construction Cost for Search Queries	2020	SIGMOD	5.093636e-05

Outgoing Citations (Sorted by Pagerank)

Showing 0 of 0 cited papers.

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

Rank	Cited Paper	Year	Venue	Pagerank

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