Research and Innovation (R&I) ecosystems involve data and knowledge flows across enterprises, academia, funding institutions, public authorities and citizens. Key R&I data elements are currently dispersed across a multitude of distinct and heterogeneous datasets; they are often neither in structured format nor systematically shared. Designing policies that affect R&I dynamics, and monitoring their impact in time, are tasks which require to overcome the limitations imposed by the usage of separated data silos, and to provide meaningful, integrated access to data with the appropriate granularity. In such a context, ontology-mediated data management can help bringing together inputs and outcomes from a variety of sources, in an open and interoperable fashion. The paper introduces UNiCS, an OWL-based ontology that has been recently developed with the main aim of driving semantic integration and access to R&I data, and to support the development of ontology-based data access (OBDA) platforms for R&I policy and decision makers.

We present GROM, a tool conceived to handle high-level schema mappings between semantic descriptions of a source and a target database. GROM rewrites mappings between the virtual, view-based semantic schemas, in terms of mappings between the two physical databases, and then executes them. The system serves the purpose of teaching two main lessons. First, designing mappings among higher-level descriptions is often simpler than working with the original schemas. Second, as soon as the view-definition language becomes more expressive, to handle, for example, negation, the mapping problem becomes extremely challenging from the technical viewpoint, so that one needs to find a proper trade-off between expressiveness and scalability.

We present TINTIN, a tool to perform efficient integrity checking of SQL assertions in SQL Server. TINTIN rewrites each assertion into a set of standard SQL queries that, given a set of insertions and deletions of tuples, allow to incrementally compute whether this update violates the assertion or not. If one of such queries returns a non empty answer, then the assertion is violated. Efficiency is achieved by evaluating only those data and those assertions that can actually be violated according to the update. TINTIN is aimed at two different purposes. First, to show the feasibility of our approach by implementing it on a commercial relational DBMS. Second, to illustrate that the efficiency we achieve is good enough for making assertions to be used in practice.

In an object-to-relational mapping system (ORM), mapping expressions explain how to expose relational data as objects and how to store objects in tables. If mappings are sufficiently expressive, then it is possible to define lossy mappings. If a user updates an object, stores it in the database based on a lossy mapping, and then retrieves the object from the database, the user might get a different result than the updated state of the object; that is, the mapping might not "roundtrip." To avoid this, the ORM should validate that user-defined mappings roundtrip the data. However, this problem is NP-hard, so mapping validation can be very slow for large or complex mappings.

We describe a software architecture we have developed for a constructive containment checker of Entity SQL queries defined over extended ER schemas expressed in Microsoft's Entity Data Model. Our application of interest is compilation of object-to-relational mappings for Microsoft's ADO.NET Entity Framework, which has been shipping since 2007. The supported language includes several features which have been individually addressed in the past but, to the best of our knowledge, they have not been addressed all at once before. Moreover, when embarking on an implementation, we found no guidance in the literature on how to modularize the software or apply published algorithms to a commercially-supported language. This paper reports on our experience in addressing these real-world challenges.

Schema mappings define relationships between schemas in a declarative way. We demonstrate MVT, a mapping validation tool that allows the designer to ask whether the mapping has certain desirable properties. The answers to these questions will provide information on whether the mapping adequately matches the intended needs and requirements. MVT is able to deal with a highly expressive class of mappings and database schemas, which allows the use of negations, order comparisons and null values. The tool does not only provide a Boolean answer as test result, but also a feedback for that result. Depending on the tested property and on the test result, the provided feedback can be in the form of example schema instances, or in the form of an explanation, that is, highlighting the mapping assertions and schema constraints responsible for getting such a result.

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.