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COMPUTING SURVEYS 1984年第4期16卷 399-433页

作者： YU, CT CHANG, CC Univ. of Illinois at Chicago Chicago Univ. of Illinois at Chicago Chicago

In this paper various techniques for optimizing queries in distributed databases are presented. Although no attempt is made to cover all proposed algorithms on this topic, quite a few ideas extracted from existing algorithms are outlined. It is hoped that large- scale experiments will be conducted to verify the usefulness of these ideas and that they will be integrated to construct a powerful algorithm for distributed query processing. [ABSTRACT FROM AUTHOR]

关键词： Communication cyclic queries distributed query processing fragment processing heuristics join optimization performance semijoin tree queries

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Adding dynamism to OGSA-DQP: Incorporating the DynaSOAr framework in distributed query processing

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12th Euro-Par 2006 Workshops

作者： Mukherjee, Arijit Watson, Paul Univ Newcastle Sch Comp Sci Claremont TowerClaremont Rd Newcastle Upon Tyne Tyne & Wear England

ISBN: (纸本)9783540722267

OGSA-DQP is a distributed query processing system for the Grid. It uses the OGSA-DAI framework for querying individual databases and adds on top of it an infrastructure to perform distributed querying on these databases. OGSA-DQP also enables the invocation of analysis services, such as Blast, within the query itself, thereby creating a form of declarative workflow system. DynaSOAr is an infrastructure for dynamically deploying web services over a Grid or a set of networked resources. The DynaSOAr view of grid computing revolves around the concept of services, rather than jobs where services are deployed on demand to meet the changing performance requirements. This paper describes the merging of these two frameworks to enable a certain amount of dynamic deployment to take place within distributed query processing.

关键词： dynamic deployment web service grid distributed query processing

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Generating distributed query processing Plans using Genetic Algorithm

Generating Distributed Query Processing Plans using Genetic ...

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International Conference on Data Storage and Data Engineering (DSDE)

作者： Kumar, T. V. Vijay Singh, Vikram Verma, Ajay Kumar Jawaharlal Nehru Univ Sch Comp & Syst Sci New Delhi 110067 India

ISBN: (纸本)9780769539584

distributed query processing has become essential in today's scenario to address the changing business needs of users. It aims to arrive at an optimal query processing plan for a given distributed query. This is a complex process as the number of possible query processing plans grows rapidly with increase in the number of sites used, and relations accessed, by the query. Therefore, there is a need to determine optimal query processing plans among all possible plans. The approach presented in this paper attempts to generate such optimal query processing plans using genetic algorithm. As per the approach, the query plans having the required data residing close to each other are considered more efficient and, therefore, are generated. These generated query plans would result in efficient query processing. Further, experimental results show that the approach is able to generate such optimal query processing plans in a fewer number of generations.

关键词： distributed query processing Genetic Algorithm

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Generating Optimal query Plans for distributed query processing using Teacher-Learner Based Optimization 11

Generating Optimal Query Plans for Distributed Query Process...

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11th International Conference on Data Mining and Warehousing (ICDMW)

作者： Mishra, Vikash Singh, Vikram Natl Inst Technol Kurukshetra 136119 Haryana India

Modern day's queries are posed on database spread across the globe, this may impose a challenge on processing queries efficiently, and a strategy is required to generate optimal query plans. In distributed relational database systems, due to partitioning or replication on relations at multiple sites, the relations required by a query to answer, may be stored at multiple sites. This leads to an exponential increase in the number of possible equivalent alternatives or query plans for a user query. Though it is not computationally reasonable to explore exhaustively all possible query plans in a large search space, the query plan with most cost-effective option for query processing is measured necessary and must be generated for a given query. In this paper, an attempt has been made to generate such optimal query plans using parameter less optimization technique Teaching-Learner based Optimization (TLBO). The TLBO algorithm was observed to go one better than the other optimization algorithms for the multi-objective unconstrained and constrained benchmark problems. Experimental comparisons of this algorithm with the multi-objective GA based distributed query plan generation algorithm shows that for higher number of relations, the TLBO based algorithm is able to generate comparatively better quality Top-K query plans. (C) 2015 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Aggregation based genetic algorithm distributed query processing Top-K Teacher learner based optimization VEGA

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Optimizing Communication Cost in distributed query processing

Optimizing Communication Cost in Distributed Query Processin...

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作者： Abdeslem Belghoul Universite Clermont Auvergne

学位级别：博士

In this thesis, we take a complementary look to the problem of optimizing the time for commu- nicating query results in distributed query processing, by investigating the relationship between the communication time and the middleware configuration. Indeed, the middleware determines, among others, how data is divided into batches and messages before being communicated over the network. Concretely, we focus on the research question: given a query Q and a network environment, what is the best middleware configuration that minimizes the time for transferring the query result over the network? To the best of our knowledge, the database research community does not have well-established strategies for middleware tuning. We present first an intensive experimental study that emphasizes the crucial impact of mid- dleware configuration on the time for communicating query results. We focus on two middleware parameters that we empirically identified as having an important influence on the communication time: (i) the fetch size F (i. e., the number of tuples in a batch that is communicated at once to an application consuming the data) and (ii) the message size M (i. e., the size in bytes of the middleware buffer, which corresponds to the amount of data that can be communicated at once from the middleware to the network layer; a batch of F tuples can be communicated via one or several messages of M bytes). Then, we describe a cost model for estimating the communication time, which is based on how data is communicated between computation nodes. Precisely, our cost model is based on two crucial observations: (i) batches and messages are communicated differently over the network: batches are communicated synchronously, whereas messages in a batch are communicated in pipeline (asynchronously), and (ii) due to network latency, it is more expensive to communicate the first message in a batch compared to any other message that is not the first in its batch. We propose an effective strate

关键词： middleware distributed query processing communication cost model fetch size message size optimizing communication cost

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SPARQL distributed query processing over Linked Data

SPARQL Distributed Query Processing over Linked Data

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作者： Abdoul Macina Universite Cote d'Azur

学位级别：博士

Driven by the Semantic Web standards, an increasing number of RDF data sources are published and connected over the Web by data providers, leading to a large distributed linked data network. However, exploiting the wealth of these data sources is very challenging for data consumers considering the data distribution, their volume growth and data sources autonomy. In the Linked Data context, federation engines allow querying these distributed data sources by relying on distributed query processing (DQP) techniques. Nevertheless, a naive implementation of the DQP approach may generate a tremendous number of remote requests towards data sources and numerous intermediate results, thus leading to costly network communications. Furthermore, the distributed query semantics is often overlooked. query expressiveness, data partitioning, and data replication are other challenges to be taken into account. To address these challenges, we first proposed a distributed query processing semantics which preserves the SPARQL language expressiveness. Afterwards, we presented several strategies for a federated query engine that transparently addresses distributed data sources. Firstly, we proposed a federated query semantics on top of RDF and SPARQL standards, and following this, we specified the semantics of federated SPARQL queries on top of the standard SPARQL semantics through a set of rewrite rules relying on service clause. Secondly, we proposed both static and dynamic strategies for the main steps of DQP approach in order to allow transparent and efficient distributed and autonomous data sources querying, and therefore enhance the federated query processing. The static optimizations rely on the results of the source selection step in which we proposed a Sampling query-Based approach. On the one hand, based on these results we proposed a Hybrid BGP-Triple query rewriting approach which addresses both horizontal and vertical data partitions and reduces the query engines workload. On

关键词： Semantic Web Web of Data Linked Data Linked Open Data Data Integration distributed query processing Federated query evaluation SPARQL query Optimization

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A Term-Based Inverted Index Partitioning Model for Efficient distributed query processing

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ACM TRANSACTIONS ON THE WEB 2013年第3期7卷 15-15页

作者： Cambazoglu, B. Barla Kayaaslan, Enver Jonassen, Simon Aykanat, Cevdet Yahoo Res Barcelona Barcelona Spain

In a shared-nothing, distributed text retrieval system, queries are processed over an inverted index that is partitioned among a number of index servers. In practice, the index is either document-based or term-based partitioned. This choice is made depending on the properties of the underlying hardware infrastructure, query traffic distribution, and some performance and availability constraints. In query processing on retrieval systems that adopt a term-based index partitioning strategy, the high communication overhead due to the transfer of large amounts of data from the index servers forms a major performance bottleneck, deteriorating the scalability of the entire distributed retrieval system. In this work, to alleviate this problem, we propose a novel inverted index partitioning model that relies on hypergraph partitioning. In the proposed model, concurrently accessed index entries are assigned to the same index servers, based on the inverted index access patterns extracted from the past query logs. The model aims to minimize the communication overhead that will be incurred by future queries while maintaining the computational load balance among the index servers. We evaluate the performance of the proposed model through extensive experiments using a real-life text collection and a search query sample. Our results show that considerable performance gains can be achieved relative to the term-based index partitioning strategies previously proposed in literature. In most cases, however, the performance remains inferior to that attained by document-based partitioning.

关键词： Design Experimentation Performance Web search engine term-based index partitioning distributed query processing hypergraph partitioning

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Detection of Data Leaks through Large Scale distributed query processing using Machine Learning

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2021年第12期12卷 284-290页

作者： Kiranmai, M. V. S. V. Haritha, D. JNTUK UCEK Dept CSE Kakinada India

With the growth in the distributed data processing and data being the fuel for each of the processes, the query processes of the data are expected to be significantly lower. Hence, the distribution of the data is highly expected and during the distributing of the data, the chances for data leakage increases to a significant extend. The data leakage problems are not generally caused by intentional errors, rather this is caused by the higher visibility of the data over multiple clusters. Henceforth, the detection process is also very critical. Many of the parallel research attempts have demonstrated various methods for the detection and as well as the prevention methods. The works in the direction of the detection of the data leaks are highly dependent either on the historical information of the leaks or depends on the contextual importance of the data. In both the cases, the outcomes of the detection process accuracy cannot be ensured. In the other hand, the preventive measures can also turn into a reactive process for detection by reversing the principles proposed in these research outcomes, but the computational complexities are significantly higher. Thus, this work proposes a novel strategy for detection of the data leakages after the data distribution during the query processing events. This work proposes an initial Occurrence Based Rule Set Extraction method using Adaptive Threshold for generating the rulesets, further for reducing the time complexity and reducing the loss of dataset attribute information, this work introduces yet another algorithm for Dynamic Inference-based Rule Set Reduction. After the inferences are generated, finally this work deploys the Attribute Subset Equivalence-based Leak Detection mechanism for final detection of the clusters with data leaks. This work demonstrates nearly 89% accuracy for the detection process.

关键词： distributed query processing distributed data leak data leak detection attribute subset equivalence dynamic inference adaptive threshold model introduction

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Generating Optimal query Plans for distributed query processing using Teacher-Learner Based Optimization

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Procedia Computer Science 2015年 54卷 281-290页

作者： Vikash Mishra Vikram Singh National Institute of Technology Kurukshetra Haryana 136 119 India

关键词： Aggregation based genetic algorithm distributed query processing Top-K Teacher learner based optimization VEGA.

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A STATE TRANSITION MODEL FOR distributed query-processing

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ACM TRANSACTIONS ON DATABASE SYSTEMS 1986年第3期11卷 294-322页

作者： LAFORTUNE, S WONG, E UNIV CALIF BERKELEY DEPT ELECT ENGN & COMP SCIBERKELEYCA 94720

A state transition model for the optimization of query processing in a distributed database system is presented. The problem is parameterized by means of a state describing the amount of processing that has been performed at each site where the database is located. A state transition occurs each time a new join or semijoin is executed. Dynamic programming is used to compute recursively the costs of the states and the globally optimal solution, taking into account communication and local processing costs. The state transition model is general enough to account for the possibility of parallel processing among the various sites, as well as for redundancy in the database. The model also permits significant reductions of the necessary computations by taking advantage of simple additivity and site-uniformity properties of a cost model, and of clever strategies that improve on the basic dynamic programming algorithm.

关键词： distributed query processing query optimization semijoin state transition model tree queries

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