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Data Integration: A Theoretical Perspective

by Maurizio Lenzerini - Symposium on Principles of Database Systems , 2002
"... 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 interestin ..."
Abstract - Cited by 944 (45 self) - Add to MetaCart
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: Semantics and Query Answering

by Ronald Fagin, Phokion G. Kolaitis, Renee J. Miller, Lucian Popa - In ICDT , 2003
"... 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 answe ..."
Abstract - Cited by 420 (37 self) - Add to MetaCart
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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Scientific workflow management and the Kepler system. Special issue: workflow in grid systems

by Bertram Ludäscher, Ilkay Altintas, Chad Berkley, Dan Higgins, Efrat Jaeger, Matthew Jones, Edward A. Lee, Jing Tao, Yang Zhao - Concurr. Comput.: Pract. Exp , 2006
"... Many scientific disciplines are now data and information driven, and new scientific knowledge is often gained by scientists putting together data analysis and knowledge discovery “pipelines”. A related trend is that more and more scientific communities realize the benefits of sharing their data and ..."
Abstract - Cited by 285 (21 self) - Add to MetaCart
Many scientific disciplines are now data and information driven, and new scientific knowledge is often gained by scientists putting together data analysis and knowledge discovery “pipelines”. A related trend is that more and more scientific communities realize the benefits of sharing their data and computational services, and are thus contributing to a distributed data and computational community infrastructure (a.k.a. “the Grid”). However, this infrastructure is only a means to an end and scientists ideally should be bothered little with its existence. The goal is for scientists to focus on development and use of what we call scientific workflows. These are networks of analytical steps that may involve, e.g., database access

Schema mediation in peer data management systems

by Alon Halevy, Zachary G. Ives, Dan Suciu, Igor Tatarinov, Alon Y. Halevy, Zachary G. Ives, Dan Suciu, Igor Tatarinov - In Proc. of ICDE , 2003
"... permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of the University of Pennsylvania’s products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotiona ..."
Abstract - Cited by 204 (27 self) - Add to MetaCart
permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of the University of Pennsylvania’s products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to
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Data Exchange: Getting to the Core

by Ronald Fagin, Phokion G. Kolaitis, Lucian Popa , 2003
"... 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 sat ..."
Abstract - Cited by 174 (19 self) - Add to MetaCart
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...

A vision for management of complex models

by Philip A. Bernstein, Alon Y. Levy, Rachel A. Pottinger - SIGMOD Record , 2000
"... Many problems encountered when building applications of database systems involve the manipulation of models. By “model, ” we mean a complex structure that represents a design artifact, such as a relational schema, object-oriented interface, UML model, XML DTD, web-site schema, semantic network, comp ..."
Abstract - Cited by 167 (23 self) - Add to MetaCart
Many problems encountered when building applications of database systems involve the manipulation of models. By “model, ” we mean a complex structure that represents a design artifact, such as a relational schema, object-oriented interface, UML model, XML DTD, web-site schema, semantic network, complex document, or software configuration. Many uses of models involve managing changes in models and transformations of data from one model into another. These uses require an explicit representation of “mappings ” between models. We propose to make database systems easier to use for these applications by making “model ” and “model mapping ” first-class objects with special operations that simplify their use. We call this capability model management. In addition to making the case for model management, our main contribution is a sketch of a proposed data model. The data model consists of formal, object-oriented structures for representing models and model mappings, and of high-level algebraic operations on those structures, such as matching, differencing, merging, function application, selection, inversion and instantiation. We focus on structure and semantics, not implementation. 1

Piazza: Data Management Infrastructure for Semantic Web

by Alon Halevy, Zachary Ives, Peter Mork, Igor Tatarinov , 2003
"... The Semantic Web envisions a World Wide Web in which data is described with rich semantics and applications can pose complex queries. To this point, researchers have defined new languages for specifying meanings for concepts and developed techniques for reasoning about them, using RDF as the data mo ..."
Abstract - Cited by 161 (12 self) - Add to MetaCart
The Semantic Web envisions a World Wide Web in which data is described with rich semantics and applications can pose complex queries. To this point, researchers have defined new languages for specifying meanings for concepts and developed techniques for reasoning about them, using RDF as the data model. To flourish, the Semantic Web needs to be able to accommodate the huge amounts of existing data and the applications operating on them. To achieve this, we are faced with two problems. First, most of the world's data is available not in RDF but in XML; XML and the applications consuming it rely not only on the domain structure of the data, but also on its document structure. Hence, to provide interoperability between such sources, we must map between both their domain structures and their document structures. Second, data management practitioners often prefer to exchange data through local point-to-point data translations, rather than mapping to common mediated schemas or ontologies.
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On the decidability and complexity of query answering over inconsistent and incomplete databases

by Andrea Calì, Domenico Lembo, Riccardo Rosati - In Proc. of PODS 2003 , 2003
"... In databases with integrity constraints, data may not satisfy the constraints. In this paper, we address the problem of obtaining consistent answers in such a setting, when key and inclusion dependencies are expressed on the database schema. We establish decidability and complexity results for query ..."
Abstract - Cited by 150 (28 self) - Add to MetaCart
In databases with integrity constraints, data may not satisfy the constraints. In this paper, we address the problem of obtaining consistent answers in such a setting, when key and inclusion dependencies are expressed on the database schema. We establish decidability and complexity results for query answering under different assumptions on data (soundness and/or completeness). In particular, after showing that the problem is in general undecidable, we identify the maximal class of inclusion dependencies under which query answering is decidable in the presence of key dependencies. Although obtained in a single database context, such results are directly applicable to data integration, where multiple information sources may provide data that are inconsistent with respect to the global view of the sources. 1.
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Composing Mappings among Data Sources

by Jayant Madhavan, Alon Y. Halevy - In VLDB , 2003
"... 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 ..."
Abstract - Cited by 138 (9 self) - Add to MetaCart
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.

Representing and Reasoning about Mappings between Domain Models

by Jayant Madhavan, Philip A. Bernstein, Pedro Domingos, Alon Y. Halevy , 2002
"... 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 m ..."
Abstract - Cited by 135 (10 self) - Add to MetaCart
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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