Guillem Rull

Validation of mappings between schemas

Mappings between schemas are key elements in several contexts such as data exchange, data integration and peer data management systems. In all these contexts, the mapping design process requires the participation of a mapping designer, who needs to be able to validate the mapping that is being defined, i.e., 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 whether they have certain desirable properties. 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 whether a query is lively over a database schema. A query is lively if there is a consistent instance of the database for which the query gives a non-empty answer. To show the feasibility of our approach, we use an implementation of the CQC method and provide some experimental results.

AuRUS: automated reasoning on UML/OCL schemas

To ensure the quality of an information system, the conceptual schema that represents its domain must be semantically correct. We present a prototype to automatically check whether a UML schema with OCL constraints is right in this sense. It is well known that the full expressiveness of OCL leads to undecidability of reasoning. To deal with this problem, our approach finds a compromise between expressiveness and decidability, thus being able to handle very expressive constraints guaranteeing termination in many cases.

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.

Ontology-based data integration in EPNet

Semantic technologies are rapidly changing the historical research. Over the last decades, an immense amount of new quantifiable data have been accumulated, and made available in interchangeable formats, in social sciences and humanities, opening up new possibilities for solving old questions and posing new ones. This paper introduces a framework that eases the access of scholars to historical and cultural data about food production and commercial trade system during the Roman Empire, distributed across different data sources. The proposed approach relies on the Ontology-Based Data Access (OBDA) paradigm, where the different datasets are virtually integrated by a conceptual layer (an ontology) that provides to the user a clear point of access and a unified and unambiguous conceptual view.

SVTe: a tool to validate database schemas giving explanations

We present SVTe, a tool to validate relational database schemas that allows performing several tests to determine the correctness of a schema. When a test is satisfied, our tool provides an example of a database state satisfying the test. Otherwise, it gives an explanation. We describe the main functionalities of SVTe, its architecture and provide some experimental results.

Semantic-Based Mappings

Data translation consists of the task of moving data from a source database to a target database. This task is usually performed by developing mappings, i.e., executable transformations from the source to the target schema. However, it is often the case that a richer description of the target database semantics is available under the form of a conceptual schema. We investigate how the mapping process changes when such a rich conceptualization of the target database is available. As a major contribution, we develop a translation algorithm that automatically rewrites a mapping from the source database schema to the target conceptual schema into an equivalent mapping from the source schema to the underlying target database schema. Experiments show that our approach scales nicely to complex conceptual schemas and large databases.

Ontology-based mappings

Data translation consists of the task of moving data from a source database to a target database. This task is usually performed by developing mappings, i.e. executable transformations from the source to the target schema. However, a richer description of the target database semantics may be available in the form of an ontology. This is typically defined as a set of views over the base tables that provides a unified conceptual view of the underlying data. We investigate how the mapping process changes when such a rich conceptualization of the target database is available. We develop a translation algorithm that automatically rewrites a mapping from the source schema to the target ontology into an equivalent mapping from the source to the target databases. Then, we show how to handle this problem when an ontology is available also for the source. Differently from previous approaches, the language we use in view definitions has the full power of non-recursive Datalog with negation. In the paper, we study the implications of adopting such an expressive language. Experiments are conducted to illustrate the trade-off between expressibility of the view language and efficiency of the chase engine used to perform the data exchange.

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.

Logic Foundations of the OCL Modelling Language

In this paper we define the first-order fragment of the Object Constraint Language (OCL), the declarative language for describing rules that apply to conceptual schemas in the Unified Modelling Language (UML). This fragment covers the whole of OCL without arithmetic operators, aggregation functions, iterators, and recursion. We give the set theoretical formal syntax and semantics in an elegant, concise, and clear way. This fragment has the same expressivity as domain-independent first-order logic (aka relational algebra), in the sense that any relational algebra expression can be reformulated as a logically equivalent OCL expression, and vice-versa.

Computing explanations for unlively queries in databases

A query is unlively if it always returns an empty answer. Debugging a database schema requires not only determining unlively queries, but also fixing them. To the best of our knowledge, the existing methods do not provide the designer with an explanation of why a query is not lively. In this paper, we propose a method for computing explanations that is independent of the particular method used to determine liveliness. It 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 calls to the underlying method. We also propose a filter to reduce the number of these calls, and experimentally compare our method with the best known method for finding unsatisfiable subsets of constraints.