U2 – 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 esti- mate the impact of the unknown data (a.k.a., unknown un- knowns) on simple aggregate queries. The key idea is that the overlap between different data sources enables us to es- timate 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 un- known unknowns is invaluable to better assess the results of aggregate queries over integrated data sources.