一句话：提出保持指定结果不变的工作流输入约减方法，用于定位科学数据分析中的异常输入或错误假设。
科学数据分析工作流常涉及探索性分析，其结果难以解释和判定正确性，用户常发现可疑结果但难以定位原因。本文倡导一种半自动方法：在保持用户指定的关注结果不变的前提下，对工作流输入数据进行约减，从而缩小搜索空间，帮助发现损坏的输入数据或对数据的错误假设。作者还阐述了在数据分析工作流中构建“保持结果的输入约减”工程支持的设想，并将早期研究原型应用于来自数据科学与机器学习在线社区的计算笔记本，报告了初步结果。
Analysis of data is the foundation of multiple scientific disciplines, manifesting in complex and diverse scientific data analysis workflows often involving exploratory analyses. Such analyses represent a particular case for traditional data engineering workflows, as results may be hard to interpret and judge whether they are correct or not, and where experimentation is a central theme. Oftentimes, there are certain aspects of a result which are suspicious and which should be further investigated to increase the trustworthiness of the workflow’s outcome. To this end, we advocate a semi-automated approach to reducing a workflow’s input data while preserving a specified outcome of interest, facilitating irregularity localization by narrowing down the search space for spotting corrupted input data or wrong assumptions made about it. We outline our vision on building engineering support for outcome-preserving input reduction within data analysis workflows, and report on preliminary results obtained from applying an early research prototype on a computational notebook taken from an online community of data scientists and machine learning practitioners.