Can a set of data dependencies truly be verified? This paper introduces a computational method known as the *chase* designed to rigorously test the implication of data dependencies. By operating on tableaux, similar to those employed by Aho, Sagiv, and Ullman, the chase provides a versatile framework for analyzing data relationships. It encapsulates previous tableau computation methods as specialized instances. By strategically interpreting tableaux, both as mappings and as templates for relations, the chase allows the testing of join dependencies, which encompass multivalued dependencies, and functional dependencies within a defined set. The study establishes an efficient computational method to prove data dependency implication. This method uses tableau which is a special database. The chase algorithm improves database designs. It assures consistency and validity. The outcomes have effects for database management. Also the outcomes also have implications for data integration. Finally, it provides a foundation for future research about testing data implications.
This paper, published in ACM Transactions on Database Systems, directly addresses the journal's core focus on database theory and practice. The introduction of the 'chase' computation method contributes to the ongoing development of tools and techniques for ensuring data integrity and consistency within database systems. This paper's focus on dependency testing aligns perfectly with the journal's scope.