Discovering Relaxed Functional Dependencies based on Multi-attribute Dominance [Extended Abstract]

To assess the quality of data it is useful to extract properties and relationships among them. However, exceptions and approximations need be considered in real-world settings. To this end, relaxed FDs (RFDs) are data dependencies accounting for both exceptions and similarities on data, but their discovery is an extremely complex problem, also due to the necessity of specifying similarity and validity thresholds. The RFD discovery algorithm presented in this paper exploits the concept of dominance to automatically derive similarity thresholds. The discovery performances and the effectiveness of the proposed algorithm are assessed through a comparative evaluation with state-of-art approaches.