Recently, there has been a renovated interest in functional dependencies due to the possibility of employing them in several advanced database operations, such as data cleaning, query relaxation, record matching, and so forth. In particular, the constraints defined for canonical functional dependencies have been relaxed to capture inconsistencies in real data, patterns of semantically related data, or semantic relationships in complex data types. In this paper, we have surveyed 35 of such functional dependencies, providing a classification criteria, motivating examples, and a systematic analysis of them.

With the advent of big data and data lakes, data are often integrated from multiple sources. Such integrated data are often of poor quality, due to inconsistencies, errors, and so forth. One way to check the quality of data is to infer functional dependencies (FDs). However, in many modern applications it might be necessary to extract properties and relationships that are not captured through FDs, due to the necessity to admit exceptions, or to consider similarity rather than equality of data values. Relaxed FDs (RFDs) have been introduced to meet these needs, but their discovery from data adds further complexity to an already complex problem, also due to the necessity of specifying similarity and validity thresholds. We propose DOMINO, a new discovery algorithm for RFDs that exploits the concept of dominance in order to derive similarity thresholds of attribute values while inferring RFDs. An experimental evaluation on real datasets demonstrates the discovery performance and the effectiveness of the proposed algorithm.