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Estimating the Impact of Unknown Unknowns on Aggregate Query Results

Yeounoh Chung; Michael Lind Mortensen; Carsten Binnig; Tim Kraska
In: ACM Transactions on Database Systems (TODS), Vol. 43, No. 1, Pages 3:1-3:37, Association for Computing Machinery (ACM), 2018.


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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