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.

• Publication year

  2021

• Venue

  IEEE International Conference on Data Engineering

• Publication date

  2021-04-01

• Fields of study

  Computer Science

• Identifiers
  DOI 10.1109/ICDE51399.2021.00263
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

• Discovering Relaxed Functional Dependencies Based on Multi-Attribute Dominance

  2021cited by this paper
• Relaxed Functional Dependencies—A Survey of Approaches

  2016cited by this paper
• Efficient discovery of similarity constraints for matching dependencies

  2013cited by this paper
• Differential dependencies: Reasoning and discovery

  2011cited by this paper
• A revival of integrity constraints for data cleaning

  2008cited by this paper

• No citing papers are available for this paper.