Bias in OLAP Queries: Detection, Explanation, and Removal
Bias in OLAP Queries: Detection, Explanation, and Removal
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DOI：
10.1145/3183713.3196914
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发表时间：
2018-03
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影响因子：
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通讯作者：
Babak Salimi;J. Gehrke;Dan Suciu
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文献类型：
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作者：
Babak Salimi;J. Gehrke;Dan Suciu
联机分析处理（OLAP）是决策支持系统的重要组成部分。OLAP工具提供了改进决策所需的洞察力和理解力。然而，OLAP查询的答案可能是有偏见的，并导致令人困惑和不正确的见解。在本文中，我们提出了一个系统来检测，解释和解决决策支持查询中的偏见。我们给出了一个简单的有偏查询的定义，它对数据执行一组独立性测试来检测偏差。我们提出了一种新的技术，给出了解释偏见，从而帮助分析师在理解发生了什么事。此外，我们开发了一个自动化的方法重写一个有偏见的查询到一个无偏见的查询，这表明分析师打算检查。在对几个真实的数据集的全面评估中，我们展示了我们技术的质量和性能，包括完全自动发现1973年著名歧视案件的革命性见解。
On line analytical processing (OLAP) is an essential element of decision-support systems. OLAP tools provide insights and understanding needed for improved decision making. However, the answers to OLAP queries can be biased and lead to perplexing and incorrect insights. In this paper, we propose, a system to detect, explain, and to resolve bias in decision-support queries. We give a simple definition of a biased query, which performs a set of independence tests on the data to detect bias. We propose a novel technique that gives explanations for bias, thus assisting an analyst in understanding what goes on. Additionally, we develop an automated method for rewriting a biased query into an unbiased query, which shows what the analyst intended to examine. In a thorough evaluation on several real datasets we show both the quality and the performance of our techniques, including the completely automatic discovery of the revolutionary insights from a famous 1973 discrimination case.