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About 9,411 papers for “Query optimization”

SPARQL Query Optimization

Stocker, Markus, Kannengießer, Annika, , Ilyas et al. · Open Research Knowledge Graph (ORKG) · 2022

Compares the performance of various algorithms evaluated using the LUBM benchmark.

Cited by 0Open access

Heuristic Query Optimization

Richard Shea May, Richard Shea · 1995

This report explains the implementation of an algorithm to optimize a QT with heuristic optimization rules. These rules were taken from [1] chapter 16 and [2] chapter 11. Heuristic optimization rules are based on properties of operations as mathematical operations in the relational algebra. Summaries of these properties can be found both in [1] and [2] also. These properties give the following heuristic rules for query optimization: 1. Perform SELECT operations as far down the tree as possible. This has the effect of reducing the number of tuples in later binary operations which are highly expensive. 2. Perform PROJECT operations as far down the tree as possible. This has the effect of reducing the number of attributes in each tuple and reduces the memory requirements, attempting to cut down on secondary storage usage. 3. Combine successive SELECT operations into one composite SELECT operation and successive PROJECT operations into one composite PROJECT operation. 4. Combine a PRODUCT followed by a SELECT into a JOIN with the selection condition in the SELECT. Also, combine a JOIN followed by a SELECT into a new JOIN which incorporates the selection condition. The remainder of this report describes an implementation of an algorithm which, given a suitable representation of a QT, optimizes according to the above heuristic rules. 2 Implementation The implementation of the heuristic optimization is done here in multiple passes. This modularity between the steps of the algorithm makes it easy to replace one or more of the existing routines with a user-supplied routine, such as might be done in an academic setting. The command line interface to the program is as follows: % Query !input file? !output file base? !input file? is the name of the input file containing the QT ...

Cited by 0

Intensional Query Optimization

Parke Godfrey, Jarek Gryz · Digital Repository at the University of Maryland (University of Maryland College Park) · 1998

We have introduced a new query optimization framework called intensional query optimization (IQO), which enables existing optimization techniques to be applied to queries that use views. In particular, we consider that view definitions may employ unions. Advanced database technologies and applications---such as federation and mediation over heterogeneous database sources---lead to such complex view definitions, and to the need to handle complex, expensive queries. Query rewriting techniques have been proposed which exploit semantic query caches, materialized views, and semantic knowledge about the database domain to optimize query evaluation. These can augment syntactic optimization to reduce evaluation costs further. Such techniques include semantic query caching, query folding, and semantic query optimization. However, most proposed rewrite techniques ignore views in queries; that is, the views are considered as other tables. The IQO framework enables rewrites to be applied to vario...

Cited by 5Open access

Multiobjective query optimization

Christos H. Papadimitriou, Mihalis Yannakakis · 2001

The optimization of queries in distributed database systems is known to be subject to delicate trade-offs. For example, the Mariposa database system allows users to specify a desired delay-cost tradeoff (that is, to supply a decreasing function u(d), specifying how much the user is willing to pay in order to receive the query results within time d); Mariposa divides a query graph into horizontal “strides,” analyzes each stride, and uses a greedy heuristic to find the “best” plan for all strides. We show that Mariposa's greedy heuristic can be arbitrarily far from the desired optimum. Applying a recent approach in multiobjective optimization algorithms to this problem, we show that the optimum cost-delay trade-off (Pareto) curve in Mariposa's framework can be approximated fast within any desired accuracy. We also present a polynomial algorithm for the general multiobjective query optimization problem, which approximates arbirarily well the optimum cost-delay tradeoff (without the restriction of Mariposa's heuristic stride subdivision).

Cited by 115

Continuous Query Optimization

Avnur, Ron, Author, Hellerstein, Joseph M, Author · UC Berkeley · 1999

In large federated and shared-nothing databases, resources can exhibit widely fluctuating characteristics. Assumptions made at the time a query is submitted will rarely hold throughout the duration of query processing. As a result, traditional static query optimization and execution techniques are ineffective in these environments. <p>In this paper we introduce a query processing mechanism called an eddy, which continuously reorders operators in a query plan as it runs. We characterize the moments of symmetry during which pipelined joins can be easily reordered, and the synchronization barriers that require inputs from different sources to be coordinated. By combining eddies with join algorithms appropriate to large-scale environments, we merge the optimization and execution phases of query processing, allowing each tuple to have a flexible ordering of the query operators. This flexibility is controlled by a combination of fluid dynamics and a simple learning algorithm. Our initial implementation demonstrates promising results, with eddies performing nearly as well as a static optimizer/executor in static scenarios, and adapting to run-time changes in the execution environment

Cited by 3

Reverse query optimization

Neha Makhija · 2025

While traditional data management typically answers questions like What were the expenses of my company this quarter?'', the underlying data can be used to answer more powerful questions likeWhat expenses should my company have this quarter (given the business requirements)?''. These questions can generally be modeled as Reverse'' Data Management (RDM) problems that ask:What optimal interventions on data produce a desired property in the output?'' Several problems from this family have been studied and used to solve challenges in domains as diverse as query explainability, data debugging, knowledge representation, and effective SQL pedagogy. However, these variants, their complexity, and practical algorithms had always been studied in isolation- hence prior solutions do not generalize to slightly different problems and settings, and many questions in this space remain unsolved. This dissertation proposes a novel paradigm for databases to compute the answers to such Reverse Questions: instead of creating specialized algorithms for easy (PTIME) and hard cases (NP-complete), we can devise unified algorithms that can solve all problem instances and are guaranteed to terminate in PTIME for easy cases. This approach allows us to separate the task of designing efficient algorithms from the task of proving tractability. An ultimate goal would be to design instance-optimal algorithms to execute Reverse Queries, however developing such algorithms is an open, challenging question for many problems across computer science with few known positive answers. Instead, in an effort to bridge this gap, we develop algorithms that are coarse-grained instance optimal i.e., they are guaranteed to run in polynomial time for all known tractable classes of instances, however they are coarse-grained instead of fine-grained i.e., they do not make guarantees on the precise running time, but provide guarantees based on if the optimal running time is known to be PTIME or not. The proposed approach leads to both practical benefits in terms of instance-based time guarantees and ease of implementation, as well as new theoretical complexity results. We show that the proposed approach can be used to solve a wide range of Reverse Data Management problems, including problems well-known in databases such as Resilience, Causal Responsibility, and many variants of Deletion Propagation. We also show that ideas of unified algorithms are applicable and useful for problems that are not traditionally considered as Reverse Data Management problems (but are closely related in the spirit of modifying query output representations), such as the problem of finding the Minimum Factorization of Provenance Expressions. A complementary contribution of this work is an ``Automatic Hardness Gadget''-finder. Showing that a problem is NP-Hard is a fundamental task in theoretical computer science (and database theory), and many problems are known to be NP-Hard, but the proofs are often tedious and not easy to find or generalize. We build a tool that leverages a semantic specification of NP-Hardness to computationally build hardness proofs and hence resolve open theoretical complexity questions.--Author's abstract

Cited by 0Open access

DISTRIBUTED QUERY OPTIMIZATION

Nicoleta IACOB · RePEc: Research Papers in Economics · 2010

The need for the distributed systems has been determined by the type of business developed by companies with offices geographically distributed where the specific organizational structure promotes a decentralized business model. This paper describes the techniques and concepts of system architecture for distributed database management systems, followed by the presentation of implementation phases involved when dealing with the distributed queries across distributed systems. The goal of query optimization is to determine the most efficient way to execute a query in a distributed environment, by obtaining a lower system response time and also by minimizing the query execution time. For this, we will analyze the factors that influence the ways to execute a query and we will also review the available strategies to optimize the distributed query execution.

Cited by 2Open access

SQL query optimization

Piyush Goel, B. R. Iyer · ACM SIGMOD Record · 1996

The strength of commercial query optimizers like DB2 comes from their ability to select an optimal order by generating all equivalent reorderings of binary operators. However, there are no known methods to generate all equivalent reorderings for a SQL query containing joins, outer joins, and groupby aggregations. Consequently, some of the reorderings with significantly lower cost may be missed. Using hypergraph model and a set of novel identities, we propose a method to reorder a SQL query containing joins, outer joins, and groupby aggregations. While these operators are sufficient to capture the SQL semantics, it is during their reordering that we identify a powerful primitive needed for a dbms. We report our findings of a simple, yet fundamental operator, generalized selection, and demonstrate its power to solve the problem of reordering of SQL queries containing joins, outer joins, and groupby aggregations.

Cited by 2

Query Query Optimization optimization by by Genetic Genetic Algorithms Algorithms

Suhail S. J. Owais, Pavel Krömer, Department Of Computer Science · 2008

Abstract. This study investigated the use of Genetic algorithms in Information retrieval in the area of optimizing a Boolean query. A query with Boolean logical operators was used in information retrieval. For Genetic algorithms, encoding chromosomes was done from Boolean query; where it was represented in the form of tree prefix with indexing for all terms and all Boolean logical operators. Information retrieval effectiveness measures precision and recall used as a fitness function in our work. Other Genetic algorithms operators were used as single point crossover on Boolean logical operators, and mutation operator was used to exchange one of the Boolean operators and, or, and xor with any other one. The goal is to retrieve most relevant documents with less number of nonrelevant documents with respect to user query in Information retrieval system using genetic programming. 1

Cited by 0

Query Optimization for Ontology-Mediated Query Answering

Wafaa El Husseini, Cheikh Brahim El Vaigh, François Goasdoué et al. · 2024

Ontology-mediated query answering (OMQA) consists in asking database queries on knowledge bases (KBs); a KB is a set of facts called the KB's database, which is described by domain knowledge called the KB's ontology. A widely-investigated OMQA technique is FO-rewriting: every query asked on a KB is reformulated w.r.t. the KB's ontology, so that its answers are computed by the relational evaluation of the query reformulation on the KB's database. Crucially, because FO-rewriting compiles the domain knowledge relevant to queries into their reformulations, query reformulations may be complex and their optimization is the crux of efficiency.

Cited by 3Open access

Novel Distributed Query Optimization Model and Hybrid Query Optimization Algorithm ABSTRACT

Deepak Sukheja · 2014

Query optimization is the most critical phase in query processing. Query optimization in distributed databases explicitly needed in many aspects of the optimization process, this is not only increases the cost of optimization, but also changes the trade-offs involved in the optimization process significantly.This paper describes the synthetically evolution of query optimization methods from uniprocessor relational database systems to parallel database systems. We point out a set of parameters to characterize and compare query optimization methods, mainly: (i) type of algorithm (static or dynamic), (ii) working environments (re-optimization or re-scheduling) and (iii) level of modification. The major contributions of this paper are: (I) Understanding the mechanisms of query optimization methods with respect to the considered environments and their constraints (e.g. parallelism, distribution, heterogeneity, large scale, dynamicity of nodes). (ii) Study the problem of query optimization particular in term of heterogeneously environment and pointing out their main characteristics, which allow comparing them and help to Implement new query optimization algorithm and model. These contributions is led to performance enhancement of query optimization in distributed database system through classify by different QEPs and minimize the response time.

Cited by 0

Query-Dependent Performance Optimization

For Vocabulary-Supported Image, Julia Vogel, Bernt Schiele · 2002

In [1], we proposed a two-stage retrieval framework which makes not only performance characterization but also performance optimization manageable. There, the performance optimization focused on the second stage of the retrieval framework. In this paper, we extend the method to a full two-stage performance characterization and optimization. In our retrieval framework, the user specifies a high-level concept to be searched for, the size of the image region to be covered by the concept (e.g.``Search images with 30-50% of sky'') and an optimization option (e.g. ``maximum recall'', ``maximum precision'' or ``joint maximization of precision and recall''). For the detection of each concept such as sky, a multitude of concept detectors exist that perform differently. In order to reach optimum retrieval performance, the detector best satisfying the user query is selected and the information of the corresponding concept detector is processed and optimized. Besides the optimization procedure itself the paper discusses the generation of multiple detectors per semantic concept. In experiments, the advantage of joint compared to individual optimization of first and second stage is shown.

Cited by 0

optimization – Sampling – Query transformation

Gennady Antoshenkov, Mohamed Ziauddin · 1994

Abstract. This paper contains an overview of the technology used in the query processing and optimization component of Oracle Rdb, a relational database management system originally developed by Digital Equipment Corporation and now under development by Oracle Corporation. Oracle Rdb is a production system that supports the most demanding database applications, runs on multiple platforms and in a variety of environments.

Cited by 0

Abstract Parametric Query Optimization

Yannis E. Ioannidis, Raymond T. Ngt, Kyuseok Shim et al. · 2008

In most database systems, the values of many impor-tant run-time parameters of the system, the data, or the query are unknown at query optimization time. Parametric query optimization attempts to identify several execution plans, each one of which is optimal for a subset of all possible values of the run-time pa-rameters. We present a general formulation of this problem and study it primarily for the buffer size pa-rameter. We adopt randomized algorithms as the main approach to this style of optimization and enhance them with a sideways information passing feature that increases their effectiveness in the new task. Experi-mental results of these enhanced algorithms show that they optimize queries for large numbers of buffer sizes in the same time needed by their conventional versions l PartiaIIy supported by NSF under PYI Grant IRI-9157368 and by grams from DEC, HP, and AT&T.

Cited by 0