用交互式网页演示如何检测修复表格数据中的真实错误。
CURED: Creating, Understanding, and Repairing Errors Demonstrator
- 构建可上传数据的Web工具，模拟真实依赖数据的错误
- 集成现代机器学习方法自动识别并清理数据错误
- 适合数据清洗研究者与需要验证数据质量的开发者
检测和清理表格数据中的错误是数据密集型软件应用的前提。近期机器学习（ML）与数据库管理系统（DBMS）交叉领域的研究揭示了统计学习算法在错误检测与清洗中的潜力。本文整合了我们在基于机器学习的数据清洗及错误模型方面的最新工作，构建了一个统一的演示系统。该Web应用允许用户上传表格数据，注入具有实际意义的数据依赖性错误，并利用现代机器学习方法进行清洗与错误机制分析。演示系统有助于弥合理论进展与直观实践洞察之间的差距，促进对表格数据错误模型与清洗算法的理解。系统已上线：https://cured.demo.calgo-lab.de/
原文摘要 · Abstract (English)
Detecting and cleaning errors in tabular data is a prerequisite for data intense software applications. Recent research at the intersection of Machine Learning (ML) and Database Management Systems (DBMS) highlights the potential of statistical learning algorithms for error detection and cleaning. This paper combines our recent work on ML-based data cleaning and error models in a unified demonstrator. The web application allows users to upload tabular data, perturb the data with realistic data dependent errors and use modern ML methods to clean and understand error mechanisms in data. Our demonstrator helps to bridge the gap between theoretical advancements and intuitive practical insights in the context of error models and data cleaning algorithms for tabular data. The demonstrator is available at https://cured.demo.calgo-lab.de/
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