Data integration is the problem of combining data residing at different sources, and providing the user with a unified view of these data. The problem of designing data integration systems is important in current real world applications, and is characterized by a number of issues that are interesting from a theoretical point of view. This document presents on overview of the material to be presented in a tutorial on data integration. The tutorial is focused on some of the theoretical issues that are relevant for data integration. Special attention will be devoted to the following aspects: modeling a data integration application, processing queries in data integration, dealing with inconsistent data sources, and reasoning on queries.

Data exchange is the problem of taking data structured under a source schema and creating an instance of a target schema that reflects the source data as accurately as possible. In this paper, we address foundational and algorithmic issues related to the semantics of data exchange and to query answering in the context of data exchange. These issues arise because, given a source instance, there may be many target instances that satisfy the constraints of the data exchange problem. We give an algebraic specification that selects, among all solutions to the data exchange problem, a special class of solutions that we call universal. A universal solution has no more and no less data than required for data exchange and it represents the entire space of possible solutions. We then identify fairly general, and practical, conditions that guarantee the existence of a universal solution and yield algorithms to compute a canonical universal solution efficiently. We adopt the notion of "certain answers" in indefinite databases for the semantics for query answering in data exchange. We investigate the computational complexity of computing the certain answers in this context and also study the problem of computing the certain answers of target queries by simply evaluating them on a canonical universal solution.

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Data exchange is the problem of taking data structured under a source schema and creating an instance of a target schema that reflects the source data as accurately as possible. Given a source instance, there may be many solutions to the data exchange problem, that is, many target instances that satisfy the constraints of the data exchange problem. In an earlier paper, we identified a special class of solutions that we call universal. A universal solution has homomorphisms into every possible solution, and hence is a "most general possible" solution. Nonetheless, given a source instance, there may be many universal solutions. This naturally raises the question of whether there is a "best" universal solution, and hence a best solution for data exchange. We answer this question by considering the well-known notion of the core of a structure, a notion that was first studied in graph theory, but has also played a role in conjunctive-query processing. The core of a structure is the smallest substructure that is also a homomorphic image of the structure. All universal solutions have the same core (up to isomorphism); we show that this core is also a universal solution, and hence the smallest universal solution. The uniqueness of the core of a universal solution together with its minimality make the core an ideal solution for data exchange. Furthermore, we show that the core is the best among all universal solutions for answering unions of conjunctive queries with inequalities. After this, we investigate the computational complexity of producing the core. Well-known results by Chandra and Merlin imply that, unless P = NP, there is no polynomial-time algorithm that, given a structure as input, returns the core of that structure as output. In contrast, in the context of data e...

Semantic mappings between data sources play a key role in several data sharing architectures. Mappings provide the relationships between data stored in different sources, and therefore enable answering queries that require data from other nodes in a data sharing network. Composing mappings is one of the core problems that lies at the heart of several optimization methods in data sharing networks, such as caching frequently traversed paths and redundancy analysis.

The data integration problem is to provide uniform access to multiple heterogeneous information sources available online (e.g., databases on the WWW). This problem has recently received considerable attention from researchers in the fields of Artificial Intelligence and Database Systems. The data integration problem is complicated by the facts that (1) sources contain closely related and overlapping data, (2) data is stored in multiple data models and schemas, and (3) data sources have differing query processing capabilities. A key element in a data integration system is the language used to describe the contents and capabilities of the data sources. While such a language needs to be as expressive as possible, it should also enable to efficiently address the main inference problem that arises in this context: to translate a user query that is formulated over a mediated schema into a query on the local schemas. This paper describes several lanaguages for describing contents of data sources, ...

Mappings between disparate models are fundamental to any application that requires interoperability between heterogeneous data and applications. Generating mappings is a laborintensive and error prone task. To build a system that helps users generate mappings, we need an explicit representation of mappings. This representation needs to have well-defined semantics to enable reasoning and comparison between mappings.

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In peer-to-peer data integration, each peer exports data in terms of its own schema, and data interoperation is achieved by means of mappings among the peer schemas. Peers are autonomous systems and mappings are dynamically created and changed. One of the challenges in these systems is answering queries posed to one peer taking into account the mappings. Obviously, query answering strongly depends on the semantics of the overall system. In this paper, we compare the commonly adopted approach of interpreting peerto-peer systems using a first-order semantics, with an alternative approach based on epistemic logic. We consider several central properties of peer-to-peer systems: modularity, generality, and decidability. We argue that the approach based on epistemic logic is superior with respect to all the above properties. In particular, we show that, in systems in which peers have decidable schemas and conjunctive mappings, but are arbitrarily interconnected, the first-order approach may lead to undecidability of query answering, while the epistemic approach always preserves decidability. This is a fundamental property, since the actual interconnections among peers are not under the control of any actor in the system. 1.

In this paper we describe a new approach to data integration which subsumes the previous approaches of local as view (LAV) and global as view (GAV). Our method, which we term both as view (BAV), is based on the use of reversible schema transformation sequences. We show how LAV and GAV view definitions can be fully derived from BA V schema transformation sequences, and how BA V transformation sequences may be partially derived from LAV or GAV view definitions. We also show how BAV supports the evolution of both global and local schemas, and we discuss ongoing implementation of the BA V approach within the AutoMed project.