Topic
Query optimization
About: Query optimization is a research topic. Over the lifetime, 17658 publications have been published within this topic receiving 474417 citations.
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Papers
Book•
2 Dec 1994
TL;DR: This book discusses Languages, Computability, and Complexity, and the Relational Model, which aims to clarify the role of Semantic Data Models in the development of Query Language Design.
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
4,638 citations
IBM1
TL;DR: System R as mentioned in this paper is an experimental database management system developed to carry out research on the relational model of data, which 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.
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.
2,347 citations
[...]
TL;DR: It is suggested that asymptotically finite information gain may be an important characteristic of good query algorithms, in which a committee of students is trained on the same data set.
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.
2,057 citations
1 Dec 2001
TL;DR: The state of the art on the problem of answering queries using views is surveyed, the algorithms proposed to solve it are described, and the disparate works into a coherent framework are synthesized.
Abstract: The problem of answering queries using views is to find efficient methods of answering a query using a set of previously defined materialized views over the database, rather than accessing the database relations. The problem has recently received significant attention because of its relevance to a wide variety of data management problems. In query optimization, finding a rewriting of a query using a set of materialized views can yield a more efficient query execution plan. To support the separation of the logical and physical views of data, a storage schema can be described using views over the logical schema. As a result, finding a query execution plan that accesses the storage amounts to solving the problem of answering queries using views. Finally, the problem arises in data integration systems, where data sources can be described as precomputed views over a mediated schema. This article surveys the state of the art on the problem of answering queries using views, and synthesizes the disparate works into a coherent framework. We describe the different applications of the problem, the algorithms proposed to solve it and the relevant theoretical results.
1,734 citations
TL;DR: It is shown that, for the DLs of the DL-Lite family, the usual DL reasoning tasks are polynomial in the size of the TBox, and query answering is LogSpace in thesize of the ABox, which is the first result ofPolynomial-time data complexity for query answering over DL knowledge bases.
Abstract: We propose a new family of description logics (DLs), called DL-Lite, specifically tailored to capture basic ontology languages, while keeping low complexity of reasoning. Reasoning here means not only computing subsumption between concepts and checking satisfiability of the whole knowledge base, but also answering complex queries (in particular, unions of conjunctive queries) over the instance level (ABox) of the DL knowledge base. We show that, for the DLs of the DL-Lite family, the usual DL reasoning tasks are polynomial in the size of the TBox, and query answering is LogSpace in the size of the ABox (i.e., in data complexity). To the best of our knowledge, this is the first result of polynomial-time data complexity for query answering over DL knowledge bases. Notably our logics allow for a separation between TBox and ABox reasoning during query evaluation: the part of the process requiring TBox reasoning is independent of the ABox, and the part of the process requiring access to the ABox can be carried out by an SQL engine, thus taking advantage of the query optimization strategies provided by current database management systems. Since even slight extensions to the logics of the DL-Lite family make query answering at least NLogSpace in data complexity, thus ruling out the possibility of using on-the-shelf relational technology for query processing, we can conclude that the logics of the DL-Lite family are the maximal DLs supporting efficient query answering over large amounts of instances.
1,691 citations
Related Topics (5)
Performance Metrics
| Year | Papers |
|---|---|
| 2025 | 164 |
| 2024 | 235 |
| 2023 | 196 |
| 2022 | 330 |
| 2021 | 132 |
| 2020 | 139 |