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About 9,411 papers for “Query optimization”

Query optimization over crowdsourced data

Hyun Jung Park, Jennifer Widom · Proceedings of the VLDB Endowment · 2013

Deco is a comprehensive system for answering declarative queries posed over stored relational data together with data obtained on-demand from the crowd. In this paper we describe Deco's cost-based query optimizer, building on Deco's data model, query language, and query execution engine presented earlier. Deco's objective in query optimization is to find the best query plan to answer a query, in terms of estimated monetary cost. Deco's query semantics and plan execution strategies require several fundamental changes to traditional query optimization. Novel techniques incorporated into Deco's query optimizer include a cost model distinguishing between "free" existing data versus paid new data, a cardinality estimation algorithm coping with changes to the database state during query execution, and a plan enumeration algorithm maximizing reuse of common subplans in a setting that makes reuse challenging. We experimentally evaluate Deco's query optimizer, focusing on the accuracy of cost estimation and the efficiency of plan enumeration.

Cited by 47

Query Optimization using Multiple Techniques

Ajay Wagh, Varsha Nemade · International Journal of Computer Applications · 2017

Query optimization is the overall process of choosing the most efficient means of executing a SQL statement. The optimizer attempts to generate the best execution plan for a SQL statement. The best execution plan is defined as the plan with the lowest cost among all considered candidate plans. SQL is a nonprocedural language, so the optimizer is free to merge, reorganize, and process in any order. The cost is a number that represents the estimated resource usage for an execution plan. The cost computation accounts for factors of query execution such as I/O, CPU, and communication. To implement query optimization methods such as Heuristic Greedy based optimization, Iterative Improvement based cost optimization and Ant Colony optimization algorithms. Show Comparison of cost, execution time and response time between Heuristic Greedy based optimization; Ant Colony Optimization and Iterative Improvement based cost optimization algorithms.

Cited by 5Open access

Query Optimization in Grid Databases

J. Murlewski, Tomasz Kowalski, Radosław Adamus et al. · 2007

This paper discusses methods for query execution and optimization in object-oriented grid databases. The queries for distributed databases have become even more complex. This cause a difficulty for query execution and optimization process, which requires advanced algorithms and techniques to reduce processing and communication cost. This paper gives the outline of distributed data processing, which is now the subject of our study and development.

Cited by 2

Enabling Incremental Query Re-Optimization

Mengmeng Liu, Zachary G. Ives, Boon Thau Loo · arXiv (Cornell University) · 2014

As declarative query processing techniques expand in scope --- to the Web, data streams, network routers, and cloud platforms --- there is an increasing need for adaptive query processing techniques that can re-plan in the presence of failures or unanticipated performance changes. A status update on the data distributions or the compute nodes may have significant repercussions on the choice of which query plan should be running. Ideally, new system architectures would be able to make cost-based decisions about reallocating work, migrating data, etc., and react quickly as real-time status information becomes available. Existing cost-based query optimizers are not incremental in nature, and must be run "from scratch" upon each status or cost update. Hence, they generally result in adaptive schemes that can only react slowly to updates. An open question has been whether it is possible to build a cost-based re-optimization architecture for adaptive query processing in a streaming or repeated query execution environment, e.g., by incrementally updating optimizer state given new cost information. We show that this can be achieved beneficially, especially for stream processing workloads. Our techniques build upon the recently proposed approach of formulating query plan enumeration as a set of recursive datalog queries; we develop a variety of novel optimization approaches to ensure effective pruning in both static and incremental cases. We implement our solution within an existing research query processing system, and show that it effectively supports cost-based initial optimization as well as frequent adaptivity.

Cited by 1Open access

Interactive preference-aware query optimization

Nathan Ong, S. E. Rojcewicz, Nicholas L. Farnan et al. · 2015

PASQL is an extension to SQL that allows users of a distributed database to specify privacy constraints on an SQL query evaluation plan. However, privacy constraints can be difficult for users to specify, and worse yet, all possible situations that could lead to a privacy violation may not be known to the user a priori. To address these challenges, we propose a GUI-based interactive process for detecting such violations and generating appropriate constraints. In this work, we demonstrate two approaches to implementing such a GUI that provide different ways of analyzing and interactively optimizing a PASQL query plan.

Cited by 2

Sampling-Based Query Re-Optimization

Wentao Wu, Jeffrey F. Naughton, Harneet Singh · arXiv (Cornell University) · 2016

Despite of decades of work, query optimizers still make mistakes on "difficult" queries because of bad cardinality estimates, often due to the interaction of multiple predicates and correlations in the data. In this paper, we propose a low-cost post-processing step that can take a plan produced by the optimizer, detect when it is likely to have made such a mistake, and take steps to fix it. Specifically, our solution is a sampling-based iterative procedure that requires almost no changes to the original query optimizer or query evaluation mechanism of the system. We show that this indeed imposes low overhead and catches cases where three widely used optimizers (PostgreSQL and two commercial systems) make large errors.

Cited by 17Open access

SQOPI: Semantic Query Optimization Framework

Mohamed MounirHassan, Ahmed Mohammed Sultan · International Journal of Computer Applications · 2014

Semantic query optimization uses semantic knowledge in databases to rewrite queries and logic programs for the purpose of more efficient query evaluation. There has been a large body of work in the area of semantic query optimization. But, unfortunately, till now no commercial application of sematic query optimization techniques has received wide attention. In this paper, we address this problem by developing a unified framework (Application Programming Interface) called SQOPI that could be used by any application developer to semantically optimize queries executed against relational database regardless of DBMS type used. Our results show that SQOPI improves both time and I/O efficiency.

Cited by 2Open access

Bringing Order to Query Optimization

Richard T. Snodgrass, Christian S. Jensen, Christian S. Jensen et al. · 2002

A variety of developments combine to highlight the need for respecting order when manipulating relations.

Cited by 0

Query Optimization With One Parameter

Anjali V. Betawadkar · 1999

Query Optimization refers to the process of selecting an efficient plan to execute a given query from among the various ways available. Each of the alternatives available to the optimizer have an associated cost estimate. Usually, either all the components of a cost estimate are known at compile time or their values are assumed and taken as constant. Plans selected using such assumptions could prove to be sub-optimal if cost parameters change significantly between compile time and run time. "Parametric Query Optimization" attempts query optimization even where some of the factors involved in cost calculation are not known till the query is executed nor can they be accurately estimated.

Cited by 0

Query Optimization on Random Databases

Silviu Laurenţiu VASILE · Studies in Informatics and Control · 2014

We analyze the cardinality of the -join operation into a random database for tables having column values following some representative types of probability distributions (uniform distribution, exponential distribution and normal distribution). The goal of our research is to use our analysis to make some query optimization on random databases. Some numerical results are studied and the conclusions we have drawn as a result of the practical experiments are presented.

Cited by 1Open access

Query Optimization for Semistructured Data

Jason McHugh, Jennifer Widom · 1997

With the emerging prevalence of semistructured data -- data that may be irregular or incomplete -- it is important to develop efficient query processing techniques for such data. This paper describes the query processor of Lore, a DBMS for semistructured data, and focuses particularly on the cost-based query optimization techniques we have developed and implemented for a semistructured environment. While all of the usual problems associated with cost-based query optimization apply to semistructured data as well, a number of additional problems arise, suchasvastly different query execution strategies for different semistructured databases, more complicated notions of database statistics, and novel uses of indexing. Weintroduce very flexible logical query plans that can be transformed into a wide varietyofphysical plans, define appropriate database statistics and a cost model, and describe plan enumeration including heuristics for reducing the search space. Our optimizer is fully implemented for most of the Lore query language, and preliminary performance results are reported.

Cited by 19

Improving query optimization of MySQL

Wangnoo, Samit · CSUN ScholarWorks (California State University, Northridge) · 2010

study the MySQL design limitations and tradeoffs;

Cited by 0Open access

Query Optimization in Teradata Warehouse

Agnieszka Gosk · Journal of Telecommunications and Information Technology · 2010

The time necessary for data processing is becoming shorter and shorter nowadays. This thesis presents a definition of the active data warehousing (ADW) paradigm. One of the data warehouses which is consistent with this paradigm is teradata warehouse. Therefore, the basic elements of the teradata architecture are described, such as processors parsing engine (PE) and access module processor (AMP). Emphasis was put on the analysis of query optimization methods. There is presented the impact of a primary index on the time of query execution. Furthermore, this paper shows different methods of optimization of data selection, data joins and data aggregation. All these methods can help to minimize the time for data processing. This paper presents experiments which show the usage of different methods of query optimization. At the end some conclusions about different index usage are included.

Cited by 0Open access

Query Optimization for Sensor Networks

Harshavardhan Achrekar, Cindy X. Chen, Benyuan Liu · 2009

This paper proposes a new approach to obtain optimality in distributed query processing in a wireless sensor network environment. The goal of this paper is to design a scheme which supports multiple data acquisition and aggregation queries and minimizes the amount of radio transmissions, average transmission time, and energy consumption. Co-related queries will be automatically rewritten into synthetic queries to minimize communication and computation costs. The query propagation and data retrieval process will also be optimized by applying dynamic route selection algorithms to individual node to decide how, when and to whom it should propagate process and transmit data. This paper provides a solution which combines various schemes applied at different phases of sensor network query optimization. The proposed approach provides significant insight into techniques which can reduce power consumption on sensor devices. traversed as the query propagates downwards into the network. However, most of these queries are issued by multiple users simultaneously while current research has focused on optimization model for a single long-running query. The problem lies in identifying what data should be collected from sensor nodes and at what frequency should it be collected. Most recent development has lead to using in-network optimization to improve performance in many-to-many aggregation for sensor streams used in controlled applications [9]. Most of the query processing sensor networks define, analyze and discuss the various aspects related to data modeling, query algebra and query optimization by attaching cost function to query [13].

Cited by 0