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About 2,804 papers for “distributed query processing”

Query processing in Distributed PIOS

Fausto Rabitti, Leonardo Benedetti, Federico Demi · 2002

An approach to query processing in object oriented distributed database systems is proposed. Distributed PIOS is a server that supports an object-oriented data model, physical data independence (i.e. different strategies for storing class hierarchies, grouping, horizontal and vertical partitioning of objects), and fragmentation transparency (i.e. transactions are not aware of the distribution of database fragments on several nodes of a computer network). The problem of the optimization of distributed queries (i.e. determining which data must be accessed at which site and which data must be transmitted among sites) is the focus of the paper.

Cited by 0

Architecture-independent distributed query processing

Hannes Mühleisen · Universitätsbibliothek der FU Berlin Hochschulschriftenstelle u. Dokumentenserver · 2013

Distributed storage systems are forced to select a trade-off between the incompatible goals Scalability, Consistency and Availability. No matter which coordination mechanism is used to create consistency, its choice greatly influences the systems performance regarding this trade-off. Furthermore, support for complex queries is highly desirable to support complex analyses without performance nightmares. However, previous proposals for distributed complex query processing are bound to a particular coordination mechanism, and are therefore difficult to adapt to new architectures. In this thesis, the goal was to investigate distributed complex query processing independent of a particular coordination mechanism and network architecture. To this end, we have developed comprehensive abstractions of the components of distributed storage systems. Also, we show an abstract method for efficient distributed query processing within this abstract environment, MMQP. Here, query processing moves through the distributed system while constantly re-optimizing its remaining task. Through a stochastic analysis, we show how logarithmic performance with regard to the average path length and the transmission costs in the distributed system is possible. To further enhance our understanding of this process, we have also performed a number of controlled experiments through simulation. A standard benchmark (TPC-H) is used for realistic data and complex queries requesting parts and aggregates of the data. The relationship between various parameters of our abstract models such as network size and the efficiency of distributed complex query processing is tested. We are able to confirm our theoretical predictions on efficiency. In summary, we show how stochastically efficient distributed query processing is possible using only a very limited of assumptions about the network, and hope that our results allow the implementation of specific implementations for large-scale distributed storage systems.

Cited by 0Open access

QoS-aware distributed query processing

Huanyi Ye, Brigitte Kerhervé, Gregor von Bochmann · 1999

In the environment of wide-area networks such as the Internet, distributed query processing is becoming problematic due to changes of the underlying networks and user requirements. In this new context, conventional query processing strategies with the homogeneous assumption will not work well, because they are unable to adapt to unexpected changes in the performance of the communication networks. In this paper, we address the issue of how to make distributed query processing aware of these changes. We introduce the idea of integrating distributed query processing with quality of service (QoS) management and accordingly illustrate our view of QoS-aware distributed query processing.

Cited by 8

Distributed query processing optimization objectives

Peter Bodorik, J. S. Riordon · 2003

The authors examine objectives, or measures of cost, which can be used in optimizing queries in a distributed database (DDB). They include the delay and dollar cost due to the network data transfer, CPU processing or a combination of both, and cost measures in terms of the size of partial results. These measures are used in distributed query processing modeling on a testbed of queries to examine the effect of choosing one measure of cost in optimizing strategies on their cost expressed in other measures and the cost of strategies generated by a two-phased approach. Results indicate that best strategies are generated when optimization considers cost measured in terms of both CPU processing and a network data transfer. They also confirm that the two-phased optimization yields close to optimal strategies.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Cited by 17

Compliant Geo-distributed Query Processing

Kaustubh Beedkar, Jorge-Arnulfo Quiané-Ruiz, Volker Markl · 2021

In this paper, we address the problem of compliant geo-distributed query processing. In particular, we focus on dataflow policies that impose restrictions on movement of data across geographical or institutional borders. Traditional ways to distributed query processing do not consider such restrictions and therefore in geo-distributed environments may lead to non-compliant query execution plans. For example, an execution plan for a query over data sources from Europe, North America, and Asia, which may otherwise be optimal, may not comply with dataflow policies as a result of shipping some restricted (intermediate) data. We pose this problem of compliance in the setting of geo-distributed query processing. We propose a compliance-based query optimizer that takes into account dataflow policies, which are declaratively specified using our policy expressions, to generate compliant geo-distributed execution plans. Our experimental study using a geo-distributed adaptation of the TPC-H benchmark data indicates that our optimization techniques are effective in generating efficient compliant plans and incur low overhead on top of traditional query optimizers.

Cited by 10

Distributed skyline trajectory query processing

Amina Belhassena, Hongzhi Wang · 2017

The massive amount of trajectory data collected from GPS has emerged in recent year. Many researchers proposed trajectory queries such as top-k query. They focused to solve them based on distance and text relevance. However, the weight of these queries are unknown. Therefore, we plan to handle the trajectory skyline query based on distance and activity keywords. Furthermore, with the huge amount of keyword semantic trajectories, user may put the wrong activity keyword to search its trajectory. Therefore, it's hard to extract the trajectory based on the exact keyword activity. In this paper, we focused to handle the trajectory fuzzy problem based on edit distance and activity weight. To accelerate the query processing, initially, we used a Distributed Mining Trajectory based on R-tree DMTR-Tree to organize the big trajectory data, then we developed an efficient algorithm to handle the trajectory skyline query. Also, for a rapid computation of the algorithm, we used the cluster computing framework of Apache Spark with MapReduce as programing model. Theoretical analysis and the experimental results show that query-processing algorithm is efficient and achieve the scalability.

Cited by 6

A Distributed Query Processing Engine

Supriyo Chatterjea, Paul Havinga · University of Twente Research Information · 2003

Wireless sensor networks (WSNs) are formed of tiny, highly energy-constrained sensor nodes that are equipped with wireless transceivers. They may be mobile and are usually deployed in large numbers in unfamiliar environments. The nodes communicate with one another by autonomously creating ad-hoc networks which are subsequently used to gather sensor data. WSNs also process the data within the network itself and only forward the result to the requesting node. This is referred to as in-network data aggregation and results in the substantial reduction of the amount of data that needs to be transmitted by any single node in the network. In this paper we present a framework for a distributed query processing engine (DQPE) which would allow sensor nodes to examine incoming queries and autonomously perform query optimisation using information available locally. Such qualities make a WSN the perfect tool to carryout environmental monitoring in future planetary exploration missions in a reliable and cost effective manner. 1.

Cited by 1Open access

QUERY PROCESSING IN DISTRIBUTED DATABASES

Victor O. K. Li · Defense Technical Information Center (DTIC) · 1981

This paper describes two new distributed query processing algorithms. The MST Algorithm minimizes the total communication costs associated with a query while the MDT Algorithm minimizes the response time. These two algorithms are easy to analyze and to implement, since they are based on the minimum spanning tree and the shortest path problems, for which numerous algorithms exist. In addition, these two algorithms can be implemented using distributed computation, i.e., each node using only information available from adjacent nodes. We also develop the 'artificial file node' technique to extend existing query processing algorithms which are designed for non-redundant databases to that for general, redundant databases. The two algorithms are illustrated by simple examples. (Author)

Cited by 11

Experiences with distributed query processing

C. Yu, C. Liu · 2002

Different implementations of an experimental distributed query processing system which is constructed on top of existing database management systems (DBMSs) are presented. A performance evaluation is carried out. It is shown that the response times of query execution are affected significantly by the degree of site autonomy, interferences among processes, interface with the local DBMSs, and communications facilities. Too much autonomy for data replication increases response times. However lack of processing autonomy also increases response times. A goal is to identify the type of autonomy that facilitates query processing. It is shown that pipelining of the actions for data replication, which include local reduction, data transfer, and union fragments, improves performance whereas pipelining between join and data replication causes deterioration in performance because of competition of resources. However, the simulation results indicate that, in a multiprocessor environment, pipelining always improves performance provided that processes which compete for the same resource are assigned to different processors.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Cited by 8

Multi-query optimization for distributed similarity query processing

Rare & Special e-Zone (The Hong Kong University of Science and Technology) · 2008

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Cited by 0

Distributed spatial query processing and optimization

Nawshad Farruque, University of Lethbridge. Faculty of Arts and Science · Open ULeth Scholarship (OPUS) (University of Lethbridge) · 2014

Applications exist today that require the management of distributed spatial data.Since spatial data is more complex than non-spatial data, performing queries on it requires more local processing (i.e.CPU and I/O) time.Also, due to geographical distribution, data transmission costs must be considered.To reduce these costs, one can employ a distributed spatial semijoin as it eliminates unnecessary objects before their transmission to other sites and the query site.Most existing work propose different representations of the distributed spatial semijoin between two sites only, with very few works exploring its use for processing a query involving more than two sites.In this thesis, we propose both new approaches for representing the spatial semijoin in a distributed setting, and their use for processing a distributed query consisting of any number of sites.Two strategies are proposed for compactly representing the spatial semijoin that reduce both the data transmission and local processing (CPU+I/O) costs when applied in a distributed spatial query.A Global Encompassing Minimum Bounding Rectangle (GEMBR) is utilized, which is partitioned, mapped and applied in two different ways to approximate the objects in a spatial joining attribute.The first is partition indices, while the second is a bit array representation.Then each spatial semijoin is applied in a multi-site distributed spatial query processing strategy.In addition, the two-site spatial semijoin is extended to handle multiple sites so that we have a benchmark strategy for comparison purposes.We have tested the query processing algorithms for four sites, which are a part of an actual working distributed system.The algorithms are compared with respect to data transmission cost, CPU time, I/O time and false positive results.The algorithms are superior in many cases at optimizing the above criteria.The bit array representation, which is called Bloom Filter Based Spatial Semijoin (BFSJ), is evaluated with respect to different filter (Thesis) degree in Computer Science from the Department of Mathematics and Computer Science at The University of Lethbridge.I am also grateful to my committee members, Dr. Yllias Chali and Dr

Cited by 0Open access