Distributed query processing (DQP) has been widely used in data intensive applications where data of relevance to users is stored in multiple locations. This paper argues: (i) that DQP can be important in the Grid, as a means of providing high-level, declarative languages for integrating data access and analysis

We present algebraic equivalences that allow to unnest nested algebraic expressions for order-preserving algebraic operators. We illustrate how these equivalences can be applied successfully to unnest nested queries given in the XQuery language. Measurements illustrate the performance gains possible by unnesting.

Even though XML was rst introduced as a schema-less, self-describing data representation

This paper presents an approach to answering queries over an ontology modelled using a description logic. The ontology acts as a global schema, providing a declarative description of the concepts of the domain, the instances of which are stored in (potentially many) object-wrapped sources. Queries are expressed using terms from the rich vocabulary of the ontology, and are translated into an equivalent calculus expression, which references only the objects available in the source databases. The query is then optimized on the basis of information from the ontology and the source databases. Distinctive features of the approach include: the use of the expressive ALCQI description logic, which supports both ontology definition and query expression; the adoption of a global-as-view approach to relating the ontology to the sources; and the use of the ontology to direct semantic optimization of queries phrased over specific sources. The approach is being developed in, and is illustrated using examples from, bioinformatics.

In the implementation of hosted business services, multiple tenants are often consolidated into the same database to reduce total cost of ownership. Common practice is to map multiple single-tenant logical schemas in the application to one multi-tenant physical schema in the database. Such mappings are challenging to create because enterprise applications allow tenants to extend the base schema, e.g., for vertical industries or geographic regions. Assuming the workload stays within bounds, the fundamental limitation on scalability for this approach is the number of tables the database can handle. To get good consolidation, certain tables must be shared among tenants and certain tables must be mapped into fixed generic structures such as Universal and Pivot Tables, which can degrade performance. This paper describes a new schema-mapping technique for multi-tenancy called Chunk Folding, where the logical tables are vertically partitioned into chunks that are folded together into different physical multi-tenant tables and joined as needed. The database’s “meta-data budget ” is divided between application-specific conventional tables and a large fixed set of generic structures called Chunk Tables. Good performance is obtained by mapping the most heavily-utilized parts of the logical schemas into the conventional tables and the remaining parts into Chunk Tables that match their structure as closely as possible. We present the results of several experiments designed to measure the efficacy of Chunk Folding and describe the multi-tenant database testbed in which these experiments were performed.

View materialization is an important technique for high performance query processing, data integration and replication. Solutions to the problem of incrementally maintaining materialized views are very relevant. So far, most work on this problem has been confined to relational settings and solutions have not been comprehensively evaluated. This paper describes MOVIE, a complete, implemented and evaluated solution to the problem of incrementally maintaining materialized OQL views in ODMG-compliant object databases. The evaluation throws light into how the e#ectiveness of incremental maintenance is a#ected by issues such as database size, and the complexity and selectivity of views.

Queries formulated in a nested way are very common in XQuery. Unfortunately, their evaluation is usually very inefficient when done in a straightforward fashion. We present a framework for handling nested queries that is based on unnesting the queries after having translated them into an algebra. We not only present a collection of algebraic equivalences, but also supply a strategy on how to use them effectively. The full potential of the approach is demonstrated by applying our rewrites to actual queries and showing that performance gains of several orders of magnitude are possible.

(ii) a component-based design in which spatial, temporal and historical extensions are formalised incrementally, for subsequent use together or separately; (iii) compatibility with mainstream query processing frameworks for object databases; and (iv) the integration of the spatio-temporal proposal with the ODMG standard. Spatio-temporal extensions to data models have been an active area of research for a number of years. To date, much of this work has focused on the relational data model, with object data models receiving far less consideration. Where descriptions of such object models do exist, there is currently a lack of systems which build upon these models to 1

Object-oriented databases (OODBs) provide powerful data abstractions and modeling facilities but they usually lack a suitable framework for query processing and optimization. Even though there is an increasing number of recent proposals on OODB query optimization, only few of them are actually focused on query optimization in the presence of object identity and destructive updates, features often supported by most realistic OODB languages. This paper presents a formal framework for optimizing object-oriented queries in the presence of side effects. These queries may contain object updates at any place and in any form. We present a language extension to the monoid comprehension calculus to express these object-oriented features and we give a formal meaning to these extensions. Our method is based on denotational semantics, which is often used to give a formal meaning to imperative programming languages. The semantics of our language extensions is expressed in terms of our monoid calculu...

The development of any comprehensive proposal for spatio-temporal databases involves significant extensions to many aspects of a non-spatio-temporal architecture. One aspect that has received less attention than most is the development of a query calculus that can be used to provide a semantics for spatio-temporal queries and underpin an effective query optimization and evaluation framework. In this paper, we show how a query calculus for spatiotemporal object databases that builds upon the monoid calculus proposed by Fegaras and Maier for ODMG-compliant database systems can be developed. The paper shows how an extension of the ODMG type system with spatial and temporal types can be accommodated into the monoid approach. It uses several queries over historical (possibly spatial) data to illustrate how, by mapping them into monoid comprehensions, the way is open for the application of a logical optimizer based on the normalization algorithm proposed by Fegaras and Maier.