Query Processing Capabilities Research Articles

Computing capabilities of mediators

Existing data-integration systems based on the mediation architecture employ a variety of mechanisms to describe the query-processing capabilities of sources. However, these systems do not compute the capabilities of the mediators based on the capabilities of the sources they integrate. In this paper, we proposed a framework to capture a rich variety of query-processing capabilities of data sources and mediators. We present algorithms to compute the set of supported queries of a mediator, based on the capability limitations of its sources. Our algorithms take into consideration a variety of query-processing techniques employed by mediators to enhance the set of supported queries.

Export database derivation in object-oriented wrappers

Wrappers export the schema and data of existing heterogeneous databases and support queries on them. In the context of cooperative information systems, we present a flexible approach to specify the derivation of object-oriented (OO) export databases from local relational databases. Our export database derivation consists of a set of extent derivation structures (EDS) which defines the extent and deep extent of export classes. Having well-defined semantics, the EDS can be readily used in transforming wrapper queries to local queries. Based on the EDS, we developed a wrapper query evaluation strategy which handles OO queries on the export databases. The strategy is unique in that it considers the limited query processing capabilities of local database systems as well as the language constraints on the local query languages.

Fast algorithms for universal quantification in large databases

Universal quantification is not supported directly in most database systems despite the fact that it adds significant power to a system's query processing and inference capabilities, in particular for the analysis of many-to-many relationships and of set-valued attributes. One of the main reasons for this omission has been that universal quantification algorithms and their performance have not been explored for large databases. In this article, we describe and compare three known algorithms and one recently proposed algorithm for relational division, the algebra operator that embodies universal quantification. For each algorithm, we investigate the performance effects of explicit duplicate removal and referential integrity enforcement, variants for inputs larger than memory, and parallel execution strategies. Analytical and experimental performance comparisons illustrate the substantial differences among the algorithms. Moreover, comparisons demonstrate that the recently proposed division algorithm evaluates a universal quantification predicate over two relations as fast as hash (semi-) join evaluates an existential quantification predicate over the same relations. Thus, existential and universal quantification can be supported with equal efficiency by adding the recently proposed algorithm to a query evaluation system. A second result of our study is that universal quantification should be expressed directly in a database query language, because most query optimizers do not recognize the rather indirect formulations available in SQL as relational division and therefore produce very poor evaluation plans for many universal quantification queries.

A temporal relational model and a query language

This paper proposes an extension of the relational data model for incorporating temporal semantics of the real world into a database. In this temporal relational model (TRM), the attributes are categorized as synchronous and asynchronous. This categorization leads to the notions of temporal dependency and time normal form. The imposition of time normal form avoids redundancy and retrieval and update anomalies. New relational algebra operations for this model are also discussed. A temporal query language called tsql, which is a superset of sql, has been proposed to retrieve information from time-varying relations. tsql has several new features, which provide extremely powerful query processing capabilities for time-varying relations. The model and the language have numerous applications, where data in their historical form are important.

Spatial query processing in an object-oriented database system

DBMSs must offer spatial query processing capabilities to meet the needs of applications such as cartography, geographic information processing and CAD. Many data structures and algorithms that process grid representations of spatial data have appeared in the literature. We unify much of this work by identifying common principles and distilling them into a small set of constructs. (Published data structures and algorithms can be derived as special cases.) We show how these constructs can be supported with only minor modifications to current DBMS implementations. The ideas are demonstrated in the context of the range query problem. Analytical and experimental evidence indicates that performance of the derived solution is very good (e.g., comparable to performance of the kd tree.)

On the foundations of the universal relation model

The universal relation model aims at achieving complete access-path independence in relational databases by relieving the user of the need for logical navigation among relations. We clarify the assumptions underlying it and explore the approaches suggested for implementing it. The essential idea of the universal relation model is that access paths are embedded in attribute names. Thus attribute names must play unique “roles.” Furthermore, it assumes that for every set of attributes there is a basic relationship that the user has in mind. The user's queries refer to these basic relationships rather than to the underlying database. Two fundamentally different approaches to the universal relation model have been taken. According to the first approach, the user's view of the database is a universal relation or many universal relations, about which the user poses queries. The second approach sees the model as having query-processing capabilities that relieve the user of the need to specify the logical access path. Thus, while the first approach gives a denotational semantics to query answering, the second approach gives it an operational semantics. We investigate the relationship between these two approaches.