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.

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, ...

Materialized views can provide massive improvements in query processing time, especially for aggregation queries over large tables. To realize this potential, the query optimizer must know how and when to exploit materialized views. This paper presents a fast and scalable algorithm for determining whether part or all of a query can be computed from materialized views and describes how it can be incorporated in transformation-based optimizers. The current version handles views composed of selections, joins and a final group-by. Optimization remains fully cost based, that is, a single “best ” rewrite is not selected by heuristic rules but multiple rewrites are generated and the optimizer chooses the best alternative in the normal way. Experimental results based on an implementation in Microsoft SQL Server show outstanding performance and scalability. Optimization time increases slowly with the number of views but remains low even up to a thousand.

This work describes an architecture for integrating heterogeneous data sources under an XML global schema, following the local-as-view approach (local sources' schemas are described as views over the global schema). In this context, we focus on the problem of translating the user's query against the XML global schema into a SQL query over the local data sources.

Abstract. We state and solve the query reformulation problem for XML publishing in a general setting that allows mixed (XML and relational) storage for the proprietary data and exploits redundancies (materialized views, indexes and caches) to enhance performance. The correspondence between published and proprietary schemas is specified by views in both directions, and the same algorithm performs rewriting-with-views, composition-with-views, or the combined effect of both, unifying the Global-As-View and Local-As-View approaches to data integration. We prove a completeness theorem which guarantees that under certain conditions, our algorithm will find a minimal reformulation if one exists. Moreover, we identify conditions when this algorithm achieves optimal complexity bounds. We solve the reformulation problem for constraints by exploiting a reduction to the problem of query reformulation. 1

The majority of web pages served today are generated dynamically, usually by an application server querying a back-end database. To enhance the scalability of dynamic content serving in large sites, application servers are offloaded to front-end nodes, called edge servers. The improvement from such application offloading is marginal, however, if data is still fetched from the origin database system. To further improve scalability and cut response times, data must be effectively cached on such edge servers. The scale of deployment of edge servers and the rising costs of their administration demand that such caches be self-managing and adaptive. In this paper, we describe DBProxy, an edge-of-network semantic data cache for web applications. DBProxy is designed to adapt to changes in the workload in a transparent and graceful fashion by caching a large number of overlapping and dynamically changing "materialized views". New "views" are added automatically while others may be discarded to save space. In this paper, we discuss the challenges of designing and implementing such a dynamic edge data cache, and describe our proposed solutions.

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, such as query optimization, the maintenance of physical data independence, data integration and data warehousing. This article surveys the theoretical issues concerning the problem of answering queries using views. 1

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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.

Abstract. Ontologies are a crucial tool for formally specifying the vocabulary and relationship of concepts used on the Semantic Web. In order to share information, agents that use different vocabularies must be able to translate data from one ontological framework to another. Ontology translation is required when translating datasets, generating ontology extensions, and querying through different ontologies. OntoMerge, an online system for ontology merging and automated reasoning, can implement ontology translation with inputs and outputs in OWL or other web languages. The merge of two related ontologies is obtained by taking the union of the concepts and the axioms defining them, and then adding bridging axioms that relate their concepts. The resulting merged ontology then serves as an inferential medium within which translation can occur. Our internal representation, Web-PDDL, is a strong typed first-order logic language for web application. Using a uniform notation for all problems allows us to factor out syntactic and semantic translation problems, and focus on the latter. Syntactic translation is done by an automatic translator between Web-PDDL and OWL or other web languages. Semantic translation is implemented using an inference engine (OntoEngine) which processes assertions and queries in Web-PDDL syntax, running in either a data-driven (forward chaining) or demand-driven (backward chaining) way. 1