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• W3137001846 abstract "Users often can see from overview-level statistics that some results look off, but are rarely able to characterize even the type of error. Reptile is an iterative human-in-the-loop explanation and cleaning system for errors in hierarchical data. Users specify an anomalous distributive aggregation result (a complaint), and Reptile recommends drill-down operations to help the user zoom-in on the underlying errors. Unlike prior explanation systems that intervene on raw records, Reptile intervenes by learning a group's expected statistics, and ranks drill-down sub-groups by how much the intervention fixes the complaint. This group-level formulation supports a wide range of error types (missing, duplicates, value errors) and uniquely leverages the distributive properties of the user complaint. Further, the learning-based intervention lets users provide domain expertise that Reptile learns from." @default.
• W3137001846 created "2021-03-29" @default.
• W3137001846 creator A5002877429 @default.
• W3137001846 creator A5049016095 @default.
• W3137001846 date "2022-06-10" @default.
• W3137001846 modified "2023-10-16" @default.
• W3137001846 title "Reptile: Aggregation-level Explanations for Hierarchical Data" @default.
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• W3137001846 doi "https://doi.org/10.1145/3514221.3517854" @default.
• W3137001846 hasPublicationYear "2022" @default.
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