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Data Integration: A Theoretical Perspective
- 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 ..."
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Cited by 965 (45 self)
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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.
Answering Queries Using Views: A Survey
, 2000
"... The problem of answering queries using views is to find efficient methods of answering a query using a set of previously defined materialized views over the database, rather than accessing the database relations. The problem has recently received significant attention because of its relevance to a w ..."
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Cited by 562 (32 self)
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The problem of answering queries using views is to find efficient methods of answering a query using a set of previously defined materialized views over the database, rather than accessing the database relations. The problem has recently received significant attention because of its relevance to a wide variety of data management problems. In query optimization, finding a rewriting of a query using a set of materialized views can yield a more efficient query execution plan. To support the separation of the logical and physical views of data, a storage schema can be described using views over the logical schema. As a result, finding a query execution plan that accesses the storage amounts to solving the problem of answering queries using views. Finally, the problem arises in data integration systems, where data sources can be described as precomputed views over a mediated schema. This article surveys the state of the art on the problem of answering queries using views, and synthesizes the disparate works into a coherent framework. We describe the different applications of the problem, the algorithms proposed to solve it and the relevant theoretical results.
Information integration using logical views
, 1997
"... A number of ideas concerning information-integration tools can be thought of as constructing answers to queries using views that represent the capabilities of information sources. We review the formal basis of these techniques, which are closely related to containment algorithms for conjunctive quer ..."
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Cited by 485 (4 self)
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A number of ideas concerning information-integration tools can be thought of as constructing answers to queries using views that represent the capabilities of information sources. We review the formal basis of these techniques, which are closely related to containment algorithms for conjunctive queries and/or Datalog programs. Then we compare the approaches taken by AT&T Labs' "Information Manifold" and the Stanford "Tsimmis" project in these terms.
Reconciling Schemas of Disparate Data Sources: A Machine-Learning Approach
- In SIGMOD Conference
, 2001
"... A data-integration system provides access to a multitude of data sources through a single mediated schema. A key bottleneck in building such systems has been the laborious manual construction of semantic mappings between the source schemas and the mediated schema. We describe LSD, a system that empl ..."
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Cited by 424 (50 self)
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A data-integration system provides access to a multitude of data sources through a single mediated schema. A key bottleneck in building such systems has been the laborious manual construction of semantic mappings between the source schemas and the mediated schema. We describe LSD, a system that employs and extends current machine-learning techniques to semi-automatically find such mappings. LSD first asks the user to provide the semantic mappings for a small set of data sources, then uses these mappings together with the sources to train a set of learners. Each learner exploits a different type of information either in the source schemas or in their data. Once the learners have been trained, LSD nds semantic mappings for a new data source by applying the learners, then combining their predictions using a meta-learner. To further improve matching accuracy, we extend machine learning techniques so that LSD can incorporate domain constraints as an additional source of knowledge, and develop a novel learner that utilizes the structural information in XML documents. Our approach thus is distinguished in that it incorporates multiple types of knowledge. Importantly, its architecture is extensible to additional learners that may exploit new kinds of information. We describe a set of experiments on several real-world domains, and show that LSD proposes semantic mappings with a high degree of accuracy.
The TSIMMIS Approach to Mediation: Data Models and Languages
- JOURNAL OF INTELLIGENT INFORMATION SYSTEMS
, 1997
"... TSIMMIS -- The Stanford-IBM Manager of Multiple Information Sources -- is a system for integrating information. It o ers a data model and a common query language that are designed to support the combining of information from many different sources. It also o ers tools for generating automatically th ..."
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Cited by 417 (9 self)
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TSIMMIS -- The Stanford-IBM Manager of Multiple Information Sources -- is a system for integrating information. It o ers a data model and a common query language that are designed to support the combining of information from many different sources. It also o ers tools for generating automatically the components that are needed to build systems for integrating information. In this paper we shall discuss the principal architectural features and their rationale.
The state of the art in distributed query processing
- ACM Computing Surveys
, 2000
"... Distributed data processing is fast becoming a reality. Businesses want to have it for many reasons, and they often must have it in order to stay competitive. While much of the infrastructure for distributed data processing is already in place (e.g., modern network technology), there are a number of ..."
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Cited by 320 (3 self)
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Distributed data processing is fast becoming a reality. Businesses want to have it for many reasons, and they often must have it in order to stay competitive. While much of the infrastructure for distributed data processing is already in place (e.g., modern network technology), there are a number of issues which still make distributed data processing a complex undertaking: (1) distributed systems can become very large involving thousands of heterogeneous sites including PCs and mainframe server machines � (2) the state of a distributed system changes rapidly because the load of sites varies over time and new sites are added to the system� (3) legacy systems need to be integrated|such legacy systems usually have not been designed for distributed data processing and now need to interact with other (modern) systems in a distributed environment. This paper presents the state of the art of query processing for distributed database and information systems. The paper presents the \textbook " architecture for distributed query processing and a series of techniques that are particularly useful for distributed database systems. These techniques include special join techniques, techniques to exploit intra-query parallelism, techniques to reduce communication costs, and techniques to exploit caching and replication of data. Furthermore, the paper discusses di erent kinds of distributed systems such as client-server, middleware (multi-tier), and heterogeneous database systems and shows how query processing works in these systems. Categories and subject descriptors: E.5 [Data]:Files � H.2.4 [Database Management Systems]: distributed databases, query processing � H.2.5 [Heterogeneous Databases]: data translation General terms: algorithms � performance Additional key words and phrases: query optimization � query execution � client-server databases � middleware � multi-tier architectures � database application systems � wrappers� replication � caching � economic models for query processing � dissemination-based information systems 1
Extracting structured data from web pages
- In ACM SIGMOD
, 2003
"... Many web sites contain a large collection of “structured” web pages. These pages encode data from an underlying structured source, and are typically generated dynamically. An example of such a collection is the set of book pages in Amazon. There are two important characteristics of such a collection ..."
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Cited by 310 (0 self)
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Many web sites contain a large collection of “structured” web pages. These pages encode data from an underlying structured source, and are typically generated dynamically. An example of such a collection is the set of book pages in Amazon. There are two important characteristics of such a collection: first, all the pages in the collection contain structured data conforming to a common schema; second, the pages are generated using a common template. Our goal is to automatically extract structured data from a collection of pages described above, without any human input like manually generated rules or training sets. Extracting structured data gives us greater querying power over the data and is useful in information integration systems. Most of the existing work on extracting structured data assumes significant human input, for example, in form of training examples of the data to be extracted. To the best of our knowledge, ROADRUNNER project is the only other work that tries to automatically extract structured data. However, ROADRUNNER makes several simplifying assumptions. These assumptions and their implications are discussed in our paper [2]. Structured data denotes data conforming to a schema or type. We borrow the definition of complex types from [1]. Any value conforming to a schema is an instance of the schema. For example, the schema ¡ £ ¥ § © ¥ � § ¥ � represents a tuple of � attributes. The first and third attributes are “atomic”; the second attribute is a set of atomic values. The value denotes an instance of schema. A template is a pattern that describes how instances of a schema are encoded. An example template for schema above � is where each letter denotes a string. Template � encodes the first attribute of between strings � and �, the second between �
Complexity of Answering Queries Using Materialized Views. In
- PODS,
, 1998
"... Abstract We study the complexity of the problem of answering queries using materialized views. This problem has attracted a lot of attention recently because of its relevance in data integration. Previous work considered only conjunctive view definitions. We examine the consequences of allowing mor ..."
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Cited by 308 (5 self)
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Abstract We study the complexity of the problem of answering queries using materialized views. This problem has attracted a lot of attention recently because of its relevance in data integration. Previous work considered only conjunctive view definitions. We examine the consequences of allowing more expressive view definition languages. The languages we consider for view definitions and user queries are: conjunctive queries with inequality, positive queries, datalog, and first-order logic. We show that the complexity of the problem depends on whether views are assumed to store all the tuples that satisfy the view definition, or only a subset of it. Finally, we apply the results to the view consistency and view self-maintainability problems which arise in data warehousing. 2
Optimizing Queries across Diverse Data Sources
- In Proc. of VLDB
, 1997
"... Businesses today need to interrelate data stored in diverse systems with differing capabilities, ideally via a single high-level query interface. We present the design of a query optimizer for Gar- lic [C+95], a middleware system designed to integrate data from a broad range of data sources with ver ..."
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Cited by 284 (15 self)
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Businesses today need to interrelate data stored in diverse systems with differing capabilities, ideally via a single high-level query interface. We present the design of a query optimizer for Gar- lic [C+95], a middleware system designed to integrate data from a broad range of data sources with very different query capabilities. Garlic's optimizer extends the rule-based approach of [Loh88 ] to work in a heterogeneous environment, by defining generic rules for the middleware and using wrapper-provided rules to encapsulate the capabilities of each data source. This approach offers great advantages in terms of plan quality, extensibility to new sources, incremental implementation of rules for new sources, and the ability to express the capabilities of a diverse set of sources. We describe the design and implementation of this optimizer, and illustrate its actions through an example.
