Denial constraints (DCs) are a generalization of many other integrity constraints (ICs) widely used in databases, such as key constraints, functional dependencies, or order dependencies. Therefore, they can serve as a unified reasoning framework for all of these ICs and express business rules that cannot be expressed by the more restrictive IC types. The process of formulating DCs by hand is difficult, because it requires not only domain expertise but also database knowledge, and due to DCs' inherent complexity, this process is tedious and error-prone. Hence, an automatic DC discovery is highly desirable: we search for all valid denial constraints in a given database instance. However, due to the large search space, the problem of DC discovery is computationally expensive. We propose a new algorithm Hydra, which overcomes the quadratic runtime complexity in the number of tuples of state-of-the-art DC discovery methods. The new algorithm's experimentally determined runtime grows only linearly in the number of tuples. This results in a speedup by orders of magnitude, especially for datasets with a large number of tuples. Hydra can deliver results in a matter of seconds that to date took hours to compute.

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We present role matching, a novel, fine-grained integrity constraint on temporal fact data, i.e., (subject, predicate, object, timestamp)-quadruples. A role is a combination of subject and predicate and can be associated with different objects as the real world evolves and the data changes over time. A role matching states that the associated object of two or more roles should always match across time. Once discovered, role matchings can serve as integrity constraints to improve data quality, for instance of structured data in Wikipedia[3]. If violated, role matchings can alert data owners or editors and thus allow them to correct the error. Finding all role matchings is challenging due both to the inherent quadratic complexity of the matching problem and the need to identify true matches based on the possibly short history of the facts observed so far. To address the first challenge, we introduce several blocking methods both for clean and dirty input data. For the second challenge, the matching stage, we show how the entity resolution method Ditto[27] can be adapted to achieve satisfactory performance for the role matching task. We evaluate our method on datasets from Wikipedia infoboxes, showing that our blocking approaches can achieve 95% recall, while maintaining a reduction ratio of more than 99.99%, even in the presence of dirty data. In the matching stage, we achieve a macro F1-score of 89% on our datasets, using automatically generated labels.

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