Query optimization

About: Query optimization is a research topic. Over the lifetime, 17658 publications have been published within this topic receiving 474417 citations.

Foundations of databases

Abstract: A. ANTECHAMBER. Database Systems. The Main Principles. Functionalities. Complexity and Diversity. Past and Future. Ties with This Book. Bibliographic Notes. Theoretical Background. Some Basics. Languages, Computability, and Complexity. Basics from Logic. The Relational Model. The Structure of the Relational Model. Named versus Unnamed Perspectives. Notation. Bibliographic Notes. B. BASICS: RELATIONAL QUERY LANGUAGES. Conjunctive Queries. Getting Started. Logic-Based Perspectives. Query Composition and Views. Algebraic Perspectives. Adding Union. Bibliographic Notes. Exercises. Adding Negation: Algebra and Calculus. The Relational Algebras. Nonrecursive Datalog with Negation. The Relational Calculus. Syntactic Restrictions for Domain Independence. Aggregate Functions. Digression: Finite Representations of Infinite Databases. Bibliographic Notes. Exercises. Static Analysis and Optimization. Issues in Practical Query Optimization. Global Optimization. Static Analysis of the Relational Calculus. Computers with Acyclic Joins. Bibliographic Notes. Exercises. Notes on Practical Languages. SQL: The Structured Query Language. Query-by-Example and Microsoft Access. Confronting the Real World. Bibliographic Notes. Exercises. C. CONSTRAINTS. Functional and Join Dependency. Motivation. Functional and Key Dependencies. join and Multivalued Dependencies. The Chase. Bibliographic Notes. Exercises. Inclusion Dependency. Inclusion Dependency in Isolation. Finite versus Infinite Implication. Nonaxiomatizability of fd's + ind's. Restricted Kinds of Inclusion Dependency. Bibliographic Notes. Exercises. A Larger Perspective. A Unifying Framework. The Chase revisited. Axiomatization. An Algebraic Perspective. Bibliographic Notes. Exercises. Design and Dependencies. Semantic Data Models. Normal Forms. Universal Relation Assumption. Bibliographic Notes. Exercises. D. DATALOG AND RECURSION. Datalog. Syntax of Datalog. Model-Theoretic Semantics. Fixpoint Semantics. Proof-Theoretic Approach. Static Program Analysis. Bibliographic Notes. Exercises. Evaluation of Datalog. Seminaive Evaluation. Top-Down Techniques. Magic. Two Improvements. Bibliographic Notes. Exercises. Recursion and Negation. Algebra + While. Calculus + Fixpoint. Datalog with Negation. Equivalence. Recursion in Practical Language. Bibliographic Notes. Exercises. Negation in Datalog. The Basic Problem. Stratified Semantics. Well-Founded Semantics. Expressive Power. Negation as Failure of Brief. Bibliographic Notes. Exercises. E. EXPRESSIVENESS AND COMPLEXITY. Sizing up Languages. Queries. Complexity of Queries. Languages and Complexity. Bibliographic Notes. Exercises. First Order, Fixpoint and While. Complexity of First-Order Queries. Expressiveness of First-Order Queries. Fixpoint and While Queries. The Impact of Order. Bibliographic Notes. Exercises. Highly Expressive Languages. While(N)-while with Arithmetic. While(new)-while with New Values. While(uty)-An Untyped Extension of while. Bibliographic Notes. Exercises. F. FINALE. Incomplete Information. Warm-Up. Weak Representation Systems. Conditional Tables. The Complexity of Nulls. Other Approaches. Bibliographic Notes. Exercises. Complex Values. Complex Value Databases. The Algebra. The Caculas. Examples. Equivalence Theorems. Fixpoint and Deduction. Expressive Power and Complexity. A Practicle Query Language for Complex Values. Bibliographic Notes. Exercises. Object Databases. Informal Presentation. Formal Definition of an OODB Model. Languages for OODB Queries. Languages for Methods. Further Issues for OODB's. Bibliographic Notes. Exercises. Dynamic Aspects. Updated Languages. Transactional Schemas. Updating Views and Deductive Databases. Active Databases. Temporal Databases and Constraints. Bibliographic Notes. Exercises. Bibliography. Symbol Index. Index. 0201537710T04062001

QBIC project: querying images by content, using color, texture, and shape

Abstract: In the query by image content (QBIC) project we are studying methods to query large on-line image databases using the images' content as the basis of the queries. Examples of the content we use include color, texture, and shape of image objects and regions. Potential applications include medical (`Give me other images that contain a tumor with a texture like this one'), photo-journalism (`Give me images that have blue at the top and red at the bottom'), and many others in art, fashion, cataloging, retailing, and industry. Key issues include derivation and computation of attributes of images and objects that provide useful query functionality, retrieval methods based on similarity as opposed to exact match, query by image example or user drawn image, the user interfaces, query refinement and navigation, high dimensional database indexing, and automatic and semi-automatic database population. We currently have a prototype system written in X/Motif and C running on an RS/6000 that allows a variety of queries, and a test database of over 1000 images and 1000 objects populated from commercially available photo clip art images. In this paper we present the main algorithms for color texture, shape and sketch query that we use, show example query results, and discuss future directions.© (1993) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Access path selection in a relational database management system

Abstract: In a high level query and data manipulation language such as SQL, requests are stated non-procedurally, without reference to access paths. This paper describes how System R chooses access paths for both simple (single relation) and complex queries (such as joins), given a user specification of desired data as a boolean expression of predicates. System R is an experimental database management system developed to carry out research on the relational model of data. System R was designed and built by members of the IBM San Jose Research Laboratory.

TinyDB: an acquisitional query processing system for sensor networks

Abstract: We discuss the design of an acquisitional query processor for data collection in sensor networks. Acquisitional issues are those that pertain to where, when, and how often data is physically acquired (sampled) and delivered to query processing operators. By focusing on the locations and costs of acquiring data, we are able to significantly reduce power consumption over traditional passive systems that assume the a priori existence of data. We discuss simple extensions to SQL for controlling data acquisition, and show how acquisitional issues influence query optimization, dissemination, and execution. We evaluate these issues in the context of TinyDB, a distributed query processor for smart sensor devices, and show how acquisitional techniques can provide significant reductions in power consumption on our sensor devices.

Query by committee

Abstract: We propose an algorithm called query by commitee, in which a committee of students is trained on the same data set. The next query is chosen according to the principle of maximal disagreement. The algorithm is studied for two toy models: the high-low game and perceptron learning of another perceptron. As the number of queries goes to infinity, the committee algorithm yields asymptotically finite information gain. This leads to generalization error that decreases exponentially with the number of examples. This in marked contrast to learning from randomly chosen inputs, for which the information gain approaches zero and the generalization error decreases with a relatively slow inverse power law. We suggest that asymptotically finite information gain may be an important characteristic of good query algorithms.