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; we also propose a parametric model that can be used instead when the data sources are imbalanced. 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.

中文翻译：

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估计未知未知数对聚合查询结果的影响

数据科学家通常会获取和整合不同的数据源以获得更高质量的结果。但是，即使有一个完美清理和合并的数据集，仍然存在两个基本问题：（1）集成数据集是否完整？(2) 任何未知（即未观察到的）数据对查询结果有何影响？在这项工作中，我们开发和分析技术来估计未知数据的影响（又名，未知的未知数) 在简单的聚合查询上。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，并且不假设有关分布的先验知识；我们还提出了一个参数模型，可以在数据源不平衡时使用。通过一系列实验，我们表明估计未知的未知数对于更好地评估集成数据源上的聚合查询的结果是非常宝贵的。