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- W4281736164 abstract "Database users, enabled by business intelligence and data visualization platforms, use relational queries to better understand their data. When surprising aggregation results or unexpected rows appear, they want to know which input table rows caused them. While state-of-the-art query explanation systems advocate for re-running queries under counterfactual interventions on the input tables, running many of them interactively remains challenging. Prior work relies on incremental view maintenance (IVM) to refresh query results, incurring significant materialization overhead for query intermediates and scaling poorly when interventions result in many insertions/deletions. Inspired by recent advances in fast fine grained lineage capture we propose a compilation engine that leverages lineage metadata to interactively evaluate counterfactual interventions. By forgoing the conventional approach of representing lineage metadata as symbolic expressions and executing them, we decrease latencies by multiple orders of magnitude against IVM and lineage based approaches." @default.
- W4281736164 created "2022-06-13" @default.
- W4281736164 creator A5050536030 @default.
- W4281736164 date "2022-06-10" @default.
- W4281736164 modified "2023-10-02" @default.
- W4281736164 title "Interactive Query Explanations Using Fine Grained Provenance" @default.
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- W4281736164 doi "https://doi.org/10.1145/3514221.3520251" @default.
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