估计未知未知数对聚合查询结果的影响

Yeounoh Chung, Michael L. Mortensen, Carsten Binnig, Tim Kraska
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引用次数: 5

摘要

数据科学家通常的做法是获取和集成不同的数据源,以获得更高质量的结果。但是,即使有一个完全清理和合并的数据集,仍然存在两个基本问题:(1)集成的数据集是完整的吗?(2)任何未知(即未观察到的)数据对查询结果的影响是什么?在这项工作中,我们开发和分析技术来估计未知数据(又称未知未知数)对简单聚合查询的影响。关键思想是,不同数据源之间的重叠使我们能够估计缺失数据项的数量和值。我们的主要技术是无参数的,不假设关于分布的先验知识。通过一系列实验,我们表明,估计未知未知数的影响对于更好地评估集成数据源上的聚合查询结果是非常宝贵的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Estimating the Impact of Unknown Unknowns on Aggregate Query Results
It is common practice for data scientists to acquire and integrate disparate data sources to achieve higher quality results. But even with a perfectly cleaned and merged data set, two fundamental questions remain: (1) is the integrated data set complete and (2) what is the impact of any unknown (i.e., unobserved) data on query results? In this work, we develop and analyze techniques to estimate the impact of the unknown data (a.k.a., unknown unknowns) on simple aggregate queries. The key idea is that the overlap between different data sources enables us to estimate the number and values of the missing data items. Our main techniques are parameter-free and do not assume prior knowledge about the distribution. Through a series of experiments, we show that estimating the impact of unknown unknowns is invaluable to better assess the results of aggregate queries over integrated data sources.
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