Reptile：层次数据的聚合级表示
Reptile: Aggregation-level Explanations for Hierarchical Data
摘要：
最近的查询解释系统通过提出描述输入记录的谓词来帮助用户理解聚合结果中的异常，如果删除输入记录，则可以解决异常。但是，用户可能很难理解谓词是如何选择的，并且这些方法仅限于可以通过删除解决的错误。相比之下，数据错误可能是由于分组错误，如丢失记录或系统值错误。本文介绍了一个层次数据解释系统Reptile。给定异常聚合查询结果，Reptile建议下一个下钻属性，并根据将组的统计信息修复为其预期值的程度对下钻组进行排名，从而解决异常。Reptile有效地训练了一个多级模型，该模型利用数据的层次结构来估计预期值，并使用特征矩阵的因子分解表示来消除由于数据的层次结构而产生的冗余。我们进一步扩展模型训练以支持因子分解数据，并开发一套利用数据分层结构的优化方法。与基于Matlab的实现相比，Reptile将端到端运行时间减少了6倍以上，正确识别了John Hopkin的COVID-19数据中的21/30数据错误，并使用哥伦比亚大学金融工具部门团队的数据和研究人员在用户研究中正确解决了20/22投诉。
英文摘要：
Recent query explanation systems help users understand anomalies in aggregation results by proposing predicates that describe input records that, if deleted, would resolve the anomalies. However, it can be difficult for users to understand how a predicate was chosen, and these approaches are limited to errors that can be resolved through deletion. In contrast, data errors may be due to group-wise errors, such as missing records or systematic value errors. This paper presents Reptile, an explanation system for hierarchical data. Given an anomalous aggregate query result, Reptile recommends the next drill-down attribute,and ranks the drill-down groups based on the extent repairing the group's statistics to its expected values resolves the anomaly. Reptile efficiently trains a multi-level model that leverages the data's hierarchy to estimate the expected values, and uses a factorised representation of the feature matrix to remove redundancies due to the data's hierarchical structure. We further extend model training to support factorised data, and develop a suite of optimizations that leverage the data's hierarchical structure. Reptile reduces end-to-end runtimes by more than 6 times compared to a Matlab-based implementation, correctly identifies 21/30 data errors in John Hopkin's COVID-19 data, and correctly resolves 20/22 complaints in a user study using data and researchers from Columbia University's Financial Instruments Sector Team.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
