Journal
SIGMOD'17: PROCEEDINGS OF THE 2017 ACM INTERNATIONAL CONFERENCE ON MANAGEMENT OF DATA
Volume -, Issue -, Pages 527-540Publisher
ASSOC COMPUTING MACHINERY
DOI: 10.1145/3035918.3064019
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Funding
- Intel Science and Technology Center for Big Data
- DARPA Award [16-43-D3M-FP-040]
- NSF CAREER Award [IIS-1453171]
- NSF [IIS-1562657, IIS-1514491]
- Air Force YIP AWARD [FA9550-15-1-0144]
- Direct For Computer & Info Scie & Enginr
- Div Of Information & Intelligent Systems [1514491] Funding Source: National Science Foundation
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Recent tools for interactive data exploration significantly increase the chance that users make false discoveries. They allow users to (visually) examine many hypotheses and make inference with simple interactions, and thus incur the issue commonly known in statistics as the multiple hypothesis testing error. In this work, we propose a solution to integrate the control of multiple hypothesis testing into interactive data exploration systems. A key insight is that existing methods for controlling the false discovery rate (such as FDR) are not directly applicable to interactive data exploration. We therefore discuss a set of new control procedures that are better suited for this task and integrate them in our system, QUDE. Via extensive experiments on both real-world and synthetic data sets we demonstrate how QUDE can help experts and novice users alike to efficiently control false discoveries.
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