带谓词约束的快速可靠的缺失数据列联分析
Fast and Reliable Missing Data Contingency Analysis with Predicate-Constraints
摘要：
今天，数据分析师在很大程度上依靠直觉来确定数据集的缺失或保留行是否会显著影响他们的分析。我们提出了一个框架，可以产生自动权变分析，即，在描述丢失数据元组的变化和频率的形式约束下，聚合SQL查询可以采用的值的范围。我们描述了如何在这些条件下处理SUM，MAX，AVG，MIN和MAX查询，从而产生具有可测试约束的硬错误界。我们提出了一种基于整数规划的优化算法，该算法将一组此类约束（即使它们重叠、冲突或无法满足）协调到此类界限中。我们在真实世界的数据集上对几个统计插补和推理基线进行的实验表明，统计技术可能具有欺骗性的高出错率，这通常是不可预测的。相比之下，我们的框架提供了硬边界，保证举行，如果不违反约束。尽管有这些硬边界，我们表现出竞争力的准确性，统计基线。
英文摘要：
Today, data analysts largely rely on intuition to determine whether missing or withheld rows of a dataset significantly affect their analyses. We propose a framework that can produce automatic contingency analysis, i.e., the range of values an aggregate SQL query could take, under formal constraints describing the variation and frequency of missing data tuples. We describe how to process SUM, COUNT, AVG, MIN, and MAX queries in these conditions resulting in hard error bounds with testable constraints. We propose an optimization algorithm based on an integer program that reconciles a set of such constraints, even if they are overlapping, conflicting, or unsatisfiable, into such bounds. Our experiments on real-world datasets against several statistical imputation and inference baselines show that statistical techniques can have a deceptively high error rate that is often unpredictable. In contrast, our framework offers hard bounds that are guaranteed to hold if the constraints are not violated. In spite of these hard bounds, we show competitive accuracy to statistical baselines.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
