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.

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Data integration: a theoretical perspective

In this paper we consider the problem of the logical characterization of the notion of consistent answer in a relational database that may violate given integrity constraints. This notion is captured in terms of the possible repaired versions of the database. A method for computing consistent answers is given and its soundness and completeness (for some classes of constraints and queries) proved. The method is based on an iterative procedure whose termination for several classes of constraints is proved as well.

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Consistent query answers in inconsistent databases

The paper considers the problem of merging several belief bases in the presence of integrity constraints and proposes a logical characterization of operators having a majority behaviour or a consensual one. Then a representation theorem in terms of pre-orders on interpretations is given. The close connection between belief revision and merging operators is shown and it is shown that the proposal extends the pure merging case (i.e. without integrity constraints) studied in a previous work. Finally it is shown that Liberatore and Schaerf commutative revision operators can be seen as a special case of merging.

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Merging Information Under Constraints: A Logical Framework

We address the problem of minimal-change integrity maintenance in the context of integrity constraints in relational databases. We assume that integrity-restoration actions are limited to tuple deletions. We focus on two basic computational issues: repair checking (is a database instance a repair of a given database?) and consistent query answers [in: ACM Symposium on Principles of Database Systems (PODS), 1999, 68] (is a tuple an answer to a given query in every repair of a given database?). We study the computational complexity of both problems, delineating the boundary between the tractable and the intractable cases. We consider denial constraints, general functional and inclusion dependencies, as well as key and foreign key constraints. Our results shed light on the computational feasibility of minimal-change integrity maintenance. The tractable cases should lead to practical implementations. The intractability results highlight the inherent limitations of any integrity enforcement mechanism, e.g., triggers or referential constraint actions, as a way of performing minimal-change integrity maintenance.

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Minimal-change integrity maintenance using tuple deletions

The problem of integrating information from conflicting sources comes up in many current applications, such as cooperative information systems, heterogeneous databases, and multiagent systems. We model this by the operation of merging first-order theories. We propose a formal semantics for this operation and show that it has desirable properties, including abiding by majority rule in case of conflict and syntax independence. We apply our semantics to the special case when the theories to be merged represent relational databases under integrity constraints. We then present a way of merging databases that have different or conflicting schemas caused by problems such as synonyms, homonyms or type conflicts mentioned in the schema integration literature.

Merging databases under constraints

In many fields such as heterogeneous databases [1] or genomic information systems [9] we are often confronted with multiple and conflicting sources of information. Systems organized around reasoning agents [12, 6] face the similar problem of resolving conflicts among contradictory knowledge or beliefs1 held by different agents. At the same time, these systems want to extract additional knowledge that is not locally held by any agent, but collectively by all of them (called implicit knowledge in [12]). For example, if an agent knows a and another agent knows a → b, then combining their knowledge yields b, even though neither one of them individually knows b.

Jinxin Lin

Papers

Merging databases under constraints

Knowledge Base Merging by Majority