A pattern-based framework for addressing data representational inconsistency

Abstract

Data representational inconsistency, where data has diverse formats or structures, is a crucial data quality problem. Existing fixing approaches either target on a specific domain or require massive information from users. In this work, we propose a user-friendly pattern-based framework for addressing data representational inconsistency. Our framework consists of three modules: pattern design, pattern detection, and pattern unification.We identify several challenges in all the three tasks in order to handle an inconsistent dataset both accurately and efficiently. We propose various techniques to tackle these issues, and our experimental results on real-life datasets demonstrate better performance of our proposals compared with existing methods.