attention has been focused on spatial databases which combine conventional and spatially related data such as Geographic Information Systems, CAD/CAM, or VLSI. A language has been developed to query such spatial databases. It recognizes the significantly different requirements of spatial data handling and overcomes the inherent problems of the application of conventional database query languages. The spatial query language has been designed as a minimal extension to the interrogative part of SQL and distinguishes from previously designed SQL extensions by (1) the preservation of SQL concepts, (2) the highlevel treatment of spatial objects, and (3) the incorporation of spatial operations and relationships. It consists of two components, a query language to describe what information to retrieve and a presentation language to specify how to display query results. Users can ask standard SQL queries to retrieve non-spatial data based on non-spatial constraints, use Spatial SQL commands to inquire about situations involving spatial data, and give instructions in the Graphical Presentation Language GPL to manipulate or examine the graphical presentation. 1 Index Terms—Geographic Information Systems, graphical presentation, query

This paper describes a customized database and a comprehensive set of queries that can be used for systematic benchmarking of relational database systems. Designing this database and a set of carefully tuned benchmarks represents a first attempt in developing a scientific methodology for performance evaluation of database management systems. We have used this database to perform a comparative evaluation of the database machine DIRECT, the "university " and "commercial " versions of the INGRES database system, the relational database system ORACLE, and the IDM 500 database machine. We present a subset of our measurements (for the single user case only), that constitute a preliminary performance evaluation of these systems. NOTE TO THE READER It is important for the reader to recognize that the results presented in this paper represent the performance of the various database systems at ONE point in time and that new releases of the various systems will undoubtably perform differently. The objective of this research was not to make a definitive statement as to which is the best relational database system on the market today. Rather, our goal was to develop a standard set of benchmarks that could be used by database system designers for evaluating changes to their systems and by users for selecting the system which best suits their needs. It is also imperative that the reader understands that the results presented in no way measure the performance of the various systems in a multiuser environment. We are currently developing a methodology for benchmarking database systems in this environment.

This paper describes the Query Rewrite facility of the Starburst extensible database system, a novel phase of query optimization. We present a suite of rewrite rules used in Starburst to transform queries into equivalent queries for faster execution, and also describe the production rule engine which is used by Starburst to choose and execute these rules. Examples are provided demonstrating that these Query Rewrite transformations lead to query execution time improvements of orders of magnitude, suggesting that Query Rewrite in general --- and these rewrite rules in particular --- are an essential step in query optimization for modern database systems. 1 Introduction In traditional database systems, query optimization typically consists of a single phase of processing in which access methods, join orders and join methods are chosen to provide an efficient plan for executing a user's declarative query. We refer to this phase as plan optimization. In this paper we present a distinct ph...

This paper extends earlier research on hash-join algorithms to a multiprocessor architecture. Implementations of a number of centralized join algorithms are described and measured. Evaluation of these algorithms served to verify earlier analytical results. In addition, they demonstrate that bit vector filtering provides dramatic improvement in the performance of all algorithms including the sort merge join algorithm. Multiprocessor configurations of the centralized Grace and Hybrid hash-join algorithms are also presented. Both algorithms are shown to provide linear increases in throughput with corresponding increases in processor and disk resources. 1.

In valid-time indeterminacy it is known that an event stored in a database did in fact occur, but it is not known exactly when. In this paper we extend the SQL data model and query language to support valid-time indeterminacy. We represent the occurrence time of an event with a set of possible instants, delimiting when the event might have occurred, and a probability distribution over that set. We also describe query language constructs to retrieve information in the presence of indeterminacy. These constructs enable users to specify their credibility in the underlying data and their plausibility in the relationships among that data. A denotational semantics for SQL’s select statement with optional credibility and plausibility constructs is given. We show that this semantics is reliable, in that it never produces incorrect information, is maximal, in that if it were extended to be more informative, the results may not be reliable, and reduces to the previous semantics when there is no indeterminacy. Although the extended data model and query language provide needed modeling capabilities, these extensions appear initially to carry a significant execution cost. A contribution of this paper is to demonstrate that our approach is useful and practical. An efficient representation of valid-time indeterminacy and efficient query processing algorithms are provided. The cost of

This paper explores a mechanism to support user-defined data types for columns in a relational data base system. Previous work suggested how to support new operators and new data types. The contribution of this work is to suggest ways to allow query optimization on commands which include new data types and operators and ways to allow access methods to be used for new data types. 1. INTRODUCTION The collection of built-in data types in a data base system (e.g. integer, floating point number, character string) and built-in operators (e.g. +, -, *, /) were motivated by the needs of business data processing applications. However, in many engineering applications this collection of types is not appropriate. For example, in a geographic application a user typically wants points, lines, line groups and polygons as basic data types and operators which include intersection, distance and containment. In scientific application, one requires complex numbers and time series with appropriate operat...

Strategy for processing multivariable queries in the database management system INGRES is considered. The general procedure is to decompose the query into a sequence of one-variable queries by alternating between (a) reduction: breaking off components of the query which are joined to it by a single variable, and (b) tuple substitution: substituting for one of the variables a tuple at a time. Algorithms for reduction and for choosing the variable to be substituted are given. In most cases the latter decision depends on estimation of costs; heuristic procedures for making such estimates are outlined.

As query engines are scaled and federated, they must cope with highly unpredictable and changeable environments. In the Telegraph project, we are attempting to architect and implement a continuously adaptive query engine suitable for global-area systems, massive parallelism, and sensor networks. To set the stage for our research, we present a survey of prior work on adaptive query processing, focusing on three characterizations of adaptivity: the frequency of adaptivity, the effects of adaptivity, and the extent of adaptivity. Given this survey, we sketch directions for research in the Telegraph project.

This paper describes the concepts used in the implementation of DBDSGN, an experimental physical design tool for relational databases developed at the IBM San Jose Research Laboratory. Given a workload for System R (consisting of a set of SQL statements and their execution frequencies), DBDSGN suggests physical configurations for efficient performance. Each configuration consists of a set of indices and an ordering for each table. Workload statements are evaluated only for atomic configurations of indices, which have only one index per table. Costs for any configuration can be obtained from those of the atomic configurations. DBDSGN uses information supplied by the System R optimizer both to determine which columns might be worth indexing and to obtain estimates of the cost of executing statements in different configurations. The tool finds efficient solutions to the index-selection problem; if we assume the cost estimates supplied by the optimizer are the actual execution costs, it finds the optimal solution. Optionally, heuristics can be used to reduce execution time. The approach taken by DBDSGN in solving the index-selection problem for multiple-table statements significantly reduces the complexity of the problem. DBDSGN’s principles were used in the Relational Design Tool (RDT), an IBM product based on DBDSGN, which performs design for SQL/DS, a relational system based on System R. System R actually uses DBDSGN’s suggested solutions as the tool expects because cost estimates and other necessary information can be obtained from System R using a new SQL statement, the EXPLAIN statement. This illustrates how a system can export a model of its internal assumptions and behavior so that other systems (such as tools) can share this model.

Abstract. In this paper, we describe the design and implementation of the ADMS query optimizer. This optimizer integrates query matching into optimization and generates more e cient query plans using cached results. It features data caching and pointer caching, alternative cache replacement strategies, and di erent cache update methods. A comprehensive set of experiments were conducted using a benchmark database and synthetic queries. The results showed that pointer caching and dynamic cache update strategies substantially saved query execution time and, thus, increased query throughput under situations with fair query correlation and update load. The requirement of the disk cache space is relatively small, and the extra optimization overhead introduced is more than o set by the time saved in query evaluation. 1