Efficient edit rule implication for nominal and ordinal data

Edit rule implication is an essential subtask when repairing data inconsistencies against a set of edit rules. In this paper, novel techniques to enhance the performance of this subtask are studied. Our work includes several contributions. First, we draw attention to the case of nominal edit rules in particular. We point out that in many cases, starting with a set of edit rules that is as small as possible is important to improve the performance. This could be achieved by folding edit rules together. Besides that, an enhanced nominal edit rule implication algorithm is proposed, exploiting the properties of nominal edit rules. Second, we introduce ordinal edit rules as a generalization of nominal edit rules, used to capture data inconsistencies for data measured on an ordinal scale and we propose an ordinal edit rule implication algorithm. Evaluation of our methods shows promising results for both implication algorithms, with the ordinal algorithm as best performing in general. On average, our techniques improve the state-of-the-art algorithm for edit rule implication with more than 50%. (C) 2021 Elsevier Inc. All rights reserved.

Automated summary

This paper studies novel techniques to enhance the performance of edit rule implication, an essential subtask when repairing data inconsistencies. The authors draw attention to the use of nominal and ordinal edit rules and propose enhanced algorithms for both. Evaluation results show promising improvements, with the ordinal algorithm performing the best.