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.

A major challenge in indexing unstructured hypertext databases is to automatically extract meta-data that enables structured search using topic taxonomies, circumvents keyword ambiguity, and improves the quality of search and profile-based routing and filtering. Therefore, an accurate classifier is an essential component of a hypertext database. Hyperlinks pose new problems not addressed in the extensive text classification literature. Links clearly contain highquality semantic clues that are lost upon a purely termbased classifier, but exploiting link information is non-trivial because it is noisy. Naive use of terms in the link neighborhood of a document can even degrade accuracy. Our contribution is to propose robust statistical models and a relaxation labeling technique for better classification by exploiting link information in a small neighborhood around documents. Our technique also adapts gracefully to the fraction of neighboring documents having known topics. We experimented ...

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Query processing in data integration occurs over networkbound, autonomous data sources. This requires extensions to traditional optimization and execution techniques for three reasons: there is an absence of quality statistics about the data, data transfer rates are unpredictable and bursty, and slow or unavailable data sources can often be replaced by overlapping or mirrored sources. This paper presents the Tukwila data integration system, designed to support adaptivity at its core using a two-pronged approach. Interleaved planning and execution with partial optimization allows Tukwila to quickly recover from decisions based on inaccurate estimates. During execution, Tukwila uses adaptive query operators such as the double pipelined hash join, which produces answers quickly, and the dynamic collector, which robustly and efficiently computes unions across overlapping data sources. We demonstrate that the Tukwila architecture extends previous innovations in adaptive execution (such as...

Integrated access to information that is spread over multiple, distributed, and heterogeneous sources is an important problem in many scientific and commercial domains. While much work has been done on query processing and choosing plans under cost criteria, very little is known about the important problem of incorporating the information quality aspect into query planning. In this paper we describe a framework for multidatabase query processing that fully includes the quality of information in many facets, such as completeness, timeliness, accuracy, etc. We seamlessly include information quality into a multidatabase query processor based on a view-rewriting mechanism. We model information quality at different levels to ultimately find a set of high-quality query answering plans.

The problem of answering queries using views is to nd ecient methods of answering a query using a set of previously materialized views over the database, rather than accessing the database relations. The problem has recently received signicant attention because of its relevance to a wide variety of data management problems. In query optimization, nding a rewriting of a query using a set of materialized views can yield a more ecient 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, nding 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. 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 dierent applications of the problem, the algorithms proposed to solve it and the relevant theoretical results.

We consider how to e ciently compute the overlap between all pairs of web documents. This information can be used to improve web crawlers, web archivers and in the presentation of search results, among others. We report statistics on how common replication is on the web, and on the cost of computing the above information for a relatively large subset of the web { about 24 million web pages which corresponds to about 150 Gigabytes of textual information. 1

Collection selection has been a research issue for years. Typically, in related work, precomputed statistics are employed in order to estimate the expected result quality of each collection, and subsequently the collections are ranked accordingly. Our thesis is that this simple approach is insufficient for several applications in which the collections typically overlap. This is the case, for example, for the collections built by autonomous peers crawling the web. We argue for the extension of existing quality measures using estimators of mutual overlap among collections and present experiments in which this combination outperforms CORI, a popular approach based on quality estimation. We outline our prototype implementation of a P2P web search engine, coined MINERVA 1, that allows handling large amounts of data in a distributed and self-organizing manner. We conduct experiments which show that taking overlap into account during collection selection can drastically decrease the number of collections that have to be contacted in order to reach a satisfactory level of recall, which is a great step toward the feasibility of distributed web search.

This paper reports our first set of results on managing uncertainty in data integration. We posit that data-integration systems need to handle uncertainty at three levels, and do so in a principled fashion. First, the semantic mappings between the data sources and the mediated schema may be approximate because there may be too many of them to be created and maintained or because in some domains (e.g., bioinformatics) it is not clear what the mappings should be. Second, queries to the system may be posed with keywords rather than in a structured form. Third, the data from the sources may be extracted using information extraction techniques and so may yield imprecise data. As a first step to building such a system, we introduce the concept of probabilistic schema mappings and analyze their formal foundations. We show that there are two possible semantics for such mappings: by-table semantics assumes that there exists a correct mapping but we don’t know what it is; by-tuple semantics assumes that the correct mapping may depend on the particular tuple in the source data. We present the query complexity and algorithms for answering queries in the presence of approximate schema mappings, and we describe an algorithm for efficiently computing the top-k answers to queries in such a setting. 1.

Introduction A data integration system provides a uniform interface to a multitude of data sources. Consider, for example, an data integration system providing information about movies from data sources on the World-Wide Web (WWW). There are numerous sources on the WWW concerning movies, such as the Internet Movie Database (providing comprehensive listings of movies, their casts, directors, genres, etc.), MovieLink (providing playing times of movies in US cities), and several sites providing textual reviews for selected movies. Suppose we want to find which movies directed by Woody Allen are playing tonight in Seattle, and their respective reviews. None of these data sources in isolation can answer this query. However, by combining data from multiple sources, we can answer queries like this one, and even more complex ones. To answer our query, we would first query the Internet Movie Database to obtain the list of movies directed by Woody Allen,