Discovering denial constraints

Abstract

Integrity constraints (ICs) provide a valuable tool for enforcing correct application semantics. However, designing ICs requires experts and time. Proposals for automatic discovery have been made for some formalisms, such as functional dependencies and their extension conditional functional dependencies. Unfortunately, these dependencies cannot express many common business rules. For example, an American citizen cannot have lower salary and higher tax rate than another citizen in the same state. In this paper, we tackle the challenges of discovering dependencies in a more expressive integrity constraint language, namely Denial Constraints (DCs). DCs are expressive enough to overcome the limits of previous languages and, at the same time, have enough structure to allow efficient discovery and application in several scenarios. We lay out theoretical and practical foundations for DCs, including a set of sound inference rules and a linear algorithm for implication testing. We then develop an efficient instance-driven DC discovery algorithm and propose a novel scoring function to rank DCs for user validation. Using real-world and synthetic datasets, we experimentally evaluate scalability and effectiveness of our solution.

Profiling relational data: a survey

“…For instance, it is not uncommon to find thousands of functional dependencies in a given dataset. To handle this and focus users on the most important, interesting, or surprising ones, ranking profiling results can help, as Chu et al [29] show for denial constraints. They suggest two functions, namely succinctness and coverage, to assess their interestingness.…”

“…If the distinct value sets of columns A and B are not available, we can estimate the Jaccard similarity using their MinHash signatures [38]. Conditional functional dependencies [24], [59], CTANE [47], CFUN [42], FACD [91], FastCFD [47] Inclusion dependencies [101], [87], SPIDER [14], ZigZag [102] Conditional inclusion dependencies [61], CINDERELLA [13], PLI [13] Foreign keys [123], [143] Denial constraints FastDC [29] Differential dependencies [128] Sequential dependencies [57] 5 Dependency detection…”

“…Recently, an extension of FastFDs called FastDC was proposed for discovering denial constraints, which are universally quantified first-order logic formulas that subsume Fds, Cfds, Inds and many others [29].…”

Discovering Graph Functional Dependencies

“…Discovery algorithms have been well studied for relational dependencies, e.g., FDs [27,41], CFDs [9,17] and denial constraints [10]. As remarked earlier, GFD discovery is much harder.…”

DFD

“…Despite their relevance for normalization of relations, Fd constraints gained importance for data quality and data cleaning applications. In particular, they can resemble integrity constraints [5] or denial constraints [4], which can be used to detect and repair data violations.…”