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Safe Visual Data Exploration

Zheguang Zhao; Emanuel Zgraggen; Lorenzo De Stefani; Carsten Binnig; Eli Upfal; Tim Kraska
In: Semih Salihoglu; Wenchao Zhou; Rada Chirkova; Jun Yang; Dan Suciu (Hrsg.). Proceedings of the 2017 ACM International Conference on Management of Data. ACM SIGMOD International Conference on Management of Data (SIGMOD-2017), May 14-19, Chicago, IL, USA, Pages 1671-1674, ACM, 2017.


Exploring data via visualization has become a popular way to understand complex data. Features or patterns in visualization can be perceived as relevant insights by users, even though they may actually arise from random noise. Moreover, interactive data exploration and visualization recommendation tools can examine a large number of observations, and therefore result in further increasing chance of spurious insights. Thus without proper statistical control, the risk of false discovery renders visual data exploration unsafe and makes users susceptible to questionable inference.To address these problems, we present QUDE, a visual data exploration system that interacts with users to formulate hypotheses based on visualizations and provides interactive control of false discoveries.

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