Auto-Detect: Data-Driven Error Detection in Tables - Huang, Zhipeng; He, Yeye (International Conference on Management of Data (SIGMOD), 2018)
Given a single column of values, existing approaches typically employ regex-like rules to detect errors by finding anomalous values inconsistent with others. Such techniques make local decisions based only on values in the given input column, without considering a more global notion of compatibility that can be inferred from large corpora of clean tables. We propose Auto-Detect, a statistics-based technique that leverages co-occurrence statistics from large corpora for error detection, which is a significant departure from existing rule-based methods. Our approach can automatically detect incompatible values, by leveraging an ensemble of judiciously selected generalization languages, each of which uses different generalizations and is sensitive to different types of errors. Errors so detected are based on global statistics, which is robust and aligns well with human intuition of errors. We test Auto-Detect on a large set of public Wikipedia tables, as well as proprietary enterprise Excel files. While both of these test sets are supposed to be of high-quality, Auto-Detect makes surprising discoveries of over tens of thousands of errors in both cases, which are manually verified to be of high precision (over 0.98). Our labeled benchmark set on Wikipedia tables is released for future research. The test benchmark and our labeled data has been made available on GitHub https://github.com/zphuangHKUCS/Auto-Detect-released-data to facilitate future research.