Computing explanations for unlively queries in databases

A query is lively in a database schema if it returns a non-empty answer for some database satisfying the schema. Debugging a database schema requires not only determining queries (as well as views or tables) that are not lively, but also fixing them. To make that task easier it is required to provide the designer with some explanation of why a query is not lively. To the best of our knowledge, the existing methods for liveliness checking in databases do not provide such explanations. An explanation is understood as the minimal set of constraints that are responsible for the non-liveliness of the tested query. In this paper we propose a method for computing such explanations which is independent of the particular method used to determine liveliness of a given query. Our method provides three levels of search: one explanation, a maximal set of non-overlapping explanations, and all explanations. The first two levels require only a linear number of callings to the underlying method. In addition, we propose a filter to reduce the number of callings to the underlying liveliness method. We also experimentally compare our method with a more naive method for query liveliness and with the best known method for finding unsatisfiable subsets of constraints.Postprint (published version

Providing explanations for database schema validation

We propose a new method for database schema validation that provides an explanation when it determines that a certain desirable property of a database schema does not hold. Explanations are required to give the designer a hint about the changes of the schema that are needed to fix the problem identified. Our method is an extension of the CQC method, which has been shown successful for testing such properties, and its contribution is twofold: Firstly, it is the first method that offers an explanation when the schema is not adequately defined. Secondly, the extension proposed here provides a significant efficiency improvement as far as the run-time performance of the method is concerned.Postprint (published version

AuRUS: explaining the validation of UML/OCL conceptual schemas

The validation and the verification of conceptual schemas have attracted a lot of interest during the last years, and several tools have been developed to automate this process as much as possible. This is achieved, in general, by assessing whether the schema satisfies different kinds of desirable properties which ensure that the schema is correct. In this paper we describe AuRUS, a tool we have developed to analyze UML/OCL conceptual schemas and to explain their (in)correctness. When a property is satisfied, AuRUS provides a sample instantiation of the schema showing a particular situation where the property holds. When it is not, AuRUS provides an explanation for such unsatisfiability, i.e., a set of integrity constraints which is in contradiction with the property.Peer ReviewedPostprint (author’s final draft

Computing explanations for unlively queries in databases

A query is lively in a database schema if it returns a non-empty answer for some database satisfying the schema. Debugging a database schema requires not only determining queries (as well as views or tables) that are not lively, but also fixing them. To make that task easier it is required to provide the designer with some explanation of why a query is not lively. To the best of our knowledge, the existing methods for liveliness checking in databases do not provide such explanations. An explanation is understood as the minimal set of constraints that are responsible for the non-liveliness of the tested query. In this paper we propose a method for computing such explanations which is independent of the particular method used to determine liveliness of a given query. Our method provides three levels of search: one explanation, a maximal set of non-overlapping explanations, and all explanations. The first two levels require only a linear number of callings to the underlying method. In addition, we propose a filter to reduce the number of callings to the underlying liveliness method. We also experimentally compare our method with a more naive method for query liveliness and with the best known method for finding unsatisfiable subsets of constraints