Definition Of Queries Research Articles

A survey of parallel execution strategies for transitive closure and logic programs

An important feature of database technology of the nineties is the use of parallelism for speeding up the execution of complex queries. This technology is being tested in several experimental database architectures and a few commercial systems for conventional select-project-join queries. In particular, hash-based fragmentation is used to distribute data to disks under the control of different processors in order to perform selections and joins in parallel. With the development of new query languages, and in particular with the definition of transitive closure queries and of more general logic programming queries, the new dimension of recursion has been added to query processing. Recursive queries are complex; at the same time, their regular structure is particularly suited for parallel execution, and parallelism may give a high efficiency gain. We survey the approaches to parallel execution of recursive queries that have been presented in the recent literature. We observe that research on parallel execution of recursive queries is separated into two distinct subareas, one focused on the transitive closure of Relational Algebra expressions, the other one focused on optimization of more general Datalog queries. Though the subareas seem radically different because of the approach and formalism used, they have many common features. This is not surprising, because most typical Datalog queries can be solved by means of the transitive closure of simple algebraic expressions. We first analyze the relationship between the transitive closure of expressions in Relational Algebra and Datalog programs. We then review sequential methods for evaluating transitive closure, distinguishing iterative and direct methods. We address the parallelization of these methods, by discussing various forms of parallelization. Data fragmentation plays an important role in obtaining parallel execution; we describe hash-based and semantic fragmentation. Finally, we consider Datalog queries, and present general methods for parallel rule execution; we recognize the similarities between these methods and the methods reviewed previously, when the former are applied to linear Datalog queries. We also provide a quantitative analysis that shows the impact of the initial data distribution on the performance of methods.

The INA: a simple query language with only attribute names

Current query languages, such as SQL, assume that the user is familiar with the database schema including the attribute names, types, and relation associations. When a user has imperfect knowledge of this information (or when he balks at the data-processing orientation of the required statements), he normally asks an experienced analyst to perform his and hoc query. The Intelligent Navigational Assistant (INA) was developed for the U S Army as a prototype query tool that permits the users to specify requests using only domain terms familiar to them. Once a request is made, it is converted into SQL for processing 1,2 To facilitate query formulation, the INA supports an interface that allows the user to identify attributes without relation associations (i.e., treats the data model as a universal relation). Because an attribute may appear in many relations, one of the principal tasks of the INA is the determination of the appropriate relation bindings. To aid in the selection of terms, the INA maintains a user vocabulary and provides facilities for browsing the vocabulary and examining term definitions. Thus, the INA has two primary functions it provides an easy-to-use interface for query definition, and it converts a request into SQL. The INA prototype has been implemented as a PC-resident knowledge-based system linked to a host-based DBMS. Its knowledge base is the logical schema of the target database, and the query transformation relies on the dependencies implicit in that schema. Supporting the knowledge-processing functions are the query definition interface, various tools to manage the target data model description, and facilities for communicating with other computers. The system was developed using TEDIUM@@@@, 3 and the user interface and query resolution mechanism are extensions of earlier work with Tequila 4 (which accessed the semantically-richer TEDIUM@@@@ data model) Work on the INA began in 1987 and was terminated in 1988. The system was demonstrated as a prototype with an Army-supplied logical model consisting of approximately 40 relations and 200 attributes. After query definition, reformation, and user acceptance, the SQL queries were submitted to the mainframe for processing. In those tests, the INA often produced better queries than those manually coded by analysts. The INA currently is undergoing a beta test with a much larger database schema. Its algorithms are described in reference 5, and reference 3 contains details regarding its implementation and semantic data model. Current research includes the development of improved query resolution algorithms based on an enriched semantic data model

QPF A versatile query language for a knowledge-based geographical information system

Abstract This paper presents a query language, QPF, and describes its implementation in a knowledge-based geographical information system (KGIS). Using QPF, users can retrieve both symbolic and graphical data, and browse through the entire database without prior knowledge of the contents and organization of the KGIS. Processing of complex queries, such as query by object spatial relations, is guided by knowledge represented in frames that simplifies not only query processing, but also query definition and makes querying user-friendly.

RUBRIC: A System for Rule-Based Information Retrieval

A research prototype software system for conceptual information retrieval has been developed. The goal of the system, called RUBRIC, is to provide more automated and relevant access to unformatted textual databases. The approach is to use production rules from artificial intelligence to define a hierarchy of retrieval subtopics, with fuzzy context expressions and specific word phrases at the bottom. RUBRIC allows the definition of detailed queries starting at a conceptual level, partial matching of a query and a document, selection of only the highest ranked documents for presentation to the user, and detailed explanation of how and why a particular document was selected. Initial experiments indicate that a RUBRIC rule set better matches human retrieval judgment than a standard Boolean keyword expression, given equal amounts of effort in defining each. The techniques presented may be useful in stand-alone retrieval systems, front-ends to existing information retrieval systems, or real-time document filtering and routing.