The Constructive method for query containment checking (extended version)

We present a new method that checks Query Containment for queries with negated derived atoms and/or integrity constraints. Existing methods for Query Containment checking that deal with these cases do not check actually containment but another related property called uniform containment, which is a sufficient but not necessary condition for containment. Our method can be seen as an extension of the canonical databases approach beyond the class of conjunctive queries.Postprint (published version

Validation of schema mappings with nested queries

With the emergence of the Web and the wide use of XML for representing data, the ability to map not only flat relational but also nested data has become crucial. The design of schema mappings is a semi-automatic process. A human designer is needed to guide the process, choose among mapping candidates, and successively refine the mapping. The designer needs a way to figure out whether the mapping is what was intended. Our approach to mapping validation allows the designer to check whether the mapping satisfies certain desirable properties. In this paper, we focus on the validation of mappings between nested relational schemas, in which the mapping assertions are either inclusions or equalities of nested queries. We focus on the nested relational setting since most XML’s Document Type Definitions (DTDs) can be represented in this model. We perform the validation by reasoning on the schemas and mapping definition. We take into account the integrity constraints defined on both the source and target schema.Preprin

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

Validation of mappings between schemas

Mappings between schemas are key elements in several contexts such as data exchange, data integration, peer data management systems, etc. In all these contexts, the process of designing a mapping requires the participation of a mapping designer that needs a way to validate the mapping being defined, i.e., to check whether the mapping is in fact what the designer intended. However, to date very little work has directly focused on the effective validation of schema mappings. In this paper, we present a new approach for validating schema mappings that allows the mapping designer to ask questions about the accomplishment of certain desirable properties of these mappings. We consider four properties of mappings: mapping satisfiability, mapping inference, query answerability and mapping losslessness. We reformulate these properties in terms of the problem of checking the liveliness of a derived predicate. We emphasize that this approach is independent of any particular method for liveliness checking and, to show the feasibility of our approach, we use an implementation of the CQC Method and provide some experimental results.Postprint (published version

Validation of UML conceptual schemas with operations

The purpose of validating a conceptual schema is to guarantee that it properly reflects what the user needs from an application. This task is not fully formalizable, so it is desirable to provide the designer with a set of tools that assist him or her in the validation process. A conceptual schema specifies the relevant information about the domain, and how this information changes as a result of operations. In this sense, we propose an approach to validate a UML conceptual schema by simulating the execution of the operations defined in it, so that the designer can check both that the schema is correctly defined and that it satisfies the requirements.Postprint (published version

Computing repairs for constraint violations in UML/OCL conceptual schemas

Updating the contents of an information base may violate some of the constraints defined over the schema. The classical way to deal with this problem has been to reject the requested update when its application would lead to some constraint violation. We follow here an alternative approach aimed at automatically computing the repairs of an update, i.e., the minimum additional changes that, when applied together with the requested update, bring the information base to a new state where all constraints are satisfied. Our approach is independent of the language used to define the schema and the constraints, since it is based on a logic formalization of both, although we apply it to UML and OCL because they are widely used in the conceptual modeling community. Our method can be used for maintaining the consistency of an information base after the application of some update, and also for dealing with the problem of fixing up non-executable operations. The fragment of OCL that we use to define the constraints has the same expressiveness as relational algebra and we also identify a subset of it which provides some nice properties in the repair-computation process. Experiments are conducted to analyze the efficiency of our approach.Peer ReviewedPostprint (author's final draft