Estimating the Impact of Unknown Unknowns on Aggregate Query Results

TL;DR

This paper introduces techniques to estimate how unobserved, unknown data affects aggregate query results in integrated datasets, using source overlap to infer missing data without prior distribution assumptions.

Contribution

It presents novel, parameter-free methods to quantify the impact of unknown unknowns on aggregate queries, enhancing data quality assessment.

Findings

Effective estimation of unknown data impact demonstrated

Techniques outperform baseline methods in experiments

Provides practical tools for data integration validation

Abstract

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.