ZIP: Lazy Imputation during Query Processing

Yiming Lin, S. Mehrotra
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Abstract

This paper develops a query-time missing value imputation framework, entitled ZIP, that modifies relational operators to be imputation aware in order to minimize the joint cost of imputing and query processing. The modified operators use a cost-based decision function to determine whether to invoke imputation or to defer to downstream operators to resolve missing values. The modified query processing logic ensures results with deferred imputations are identical to those produced if all missing values were imputed first. ZIP includes a novel outer-join based approach to preserve missing values during execution, and a bloom filter based index to optimize the space and running overhead. Extensive experiments on both real and synthetic data sets demonstrate 10 to 25 times improvement when augmenting the state-of-the-art technology, ImputeDB, with ZIP-based deferred imputation. ZIP also outperforms the offline approach by up to 19607 times in a real data set.
ZIP:查询处理过程中的懒惰估算
本文开发了一种名为 ZIP 的查询时缺失值归因框架,它可以修改关系运算符,使其具有归因意识,从而最大限度地降低归因和查询处理的联合成本。修改后的运算符使用基于成本的决策函数来决定是调用估算还是推迟到下游运算符来解决缺失值问题。修改后的查询处理逻辑可确保延迟估算的结果与先估算所有缺失值的结果相同。ZIP 包括一种新颖的基于外连接的方法,用于在执行过程中保留缺失值,以及一种基于 Bloom 过滤器的索引,用于优化空间和运行开销。在真实数据集和合成数据集上进行的大量实验表明,在使用基于 ZIP 的延迟估算技术增强最先进的 ImputeDB 时,效果提高了 10 到 25 倍。在真实数据集中,ZIP 的性能也比离线方法高出 19607 倍。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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