Estimating the impact of unknown unknowns on aggregate qery results

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  • Yeounoh Chung, Brown University
  • ,
  • Michael Lind Mortensen
  • Carsten Binnig, Brown University, Technische Universität Darmstadt
  • ,
  • Tim Kraska, Brown University, MIT Computer Science and Artificial Intelligence Laboratory

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 parameterfree 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.

Original languageEnglish
Article number3
JournalACM Transactions on Database Systems
Volume43
Issue1
ISSN0362-5915
DOIs
Publication statusPublished - 1 Mar 2018

    Research areas

  • Aggregate query processing, Crowdsourcing, Species estimation, Unknown unknowns

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