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检索条件"主题词=distributed query processing"

共 130 条记录，以下是81-90 订阅

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Quantifying Performance and Quality Gains in distributed Web Search Engines

Quantifying Performance and Quality Gains in Distributed Web...

引用

32nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval

作者： Barla Cambazoglu, B. Plachouras, Vassilis Baeza-Yates, Ricardo Yahoo Res Barcelona Spain

ISBN: (纸本)9781605584836

distributed search engines based on geographical partitioning of a central Web index emerge as a feasible solution to the immense growth of the Web, user bases, and query traffic. However, there is still lack of research in quantifying the performance and quality gains that can be achieved by such architectures. In this paper, we develop various cost models to evaluate the performance benefits of a geographically distributed search engine architecture based on partial index replication and query forwarding. Specifically, we focus on possible performance gains due to the distributed nature of query processing and Web crawling processes. We show that any response time gain achieved by distributed query processing can be utilized to improve search relevance as the use of complex but more accurate algorithms can now be enabled for document ranking. We also show that distributed Web crawling leads to better Web coverage and try to see if this improves the search quality. We verify the validity of Our claims over large, real-life datasets via simulations.

关键词： Data centers distributed query processing index partitioning Web crawling

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Compile-Time Code Generation for Embedded Data-Intensive query Languages 7

Compile-Time Code Generation for Embedded Data-Intensive Que...

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IEEE International Congress on Big Data (IEEE BigData) Part of the IEEE World Congress on Services

作者： Fegaras, Leonidas Noor, Md Hasanuzzaman Univ Texas Arlington CSE Arlington TX 76019 USA

ISBN: (纸本)9781538672327

Many emerging Big Data programming environments, such as Spark and fink, provide powerful APIs that are inspired by functional programming. However, because of the complexity involved in developing and fine-tuning data analysis applications using the provided APIs, many programmers prefer to use declarative languages, such as Hive and Spark SQL, to code their distributed applications. Unfortunately, current data analysis query languages, which are typically based on the relational model, cannot effectively capture the rich data types and computations required for complex data analysis applications. Furthermore, these query languages are not well-integrated with the host programming language, as they are based on an incompatible data model, and are checked for correctness at runtime, which results in a significantly longer program development time. To address these shortcomings, we introduce a new query language for data-intensive scalable computing, called DIQL, that is deeply embedded in Scala, and a query optimization framework that optimizes and translates DIQL queries to byte code at compile-time. In contrast to other query languages, our query embedding eliminates impedance mismatch as any Scala code can be seamlessly mixed with SQL-like syntax, without having to add any special declaration. DIQL supports nested collections and hierarchical data and allows query nesting at any place in a query. With DIQL, programmers can express complex data analysis tasks, such as PageRank and matrix factorization, using SQL-like syntax exclusively. The DIQL query optimizer can find any possible join in a query, including joins hidden across deeply nested queries, thus unnesting any form of query nesting. Currently, DIQL can run on three Big Data platforms: Apache Spark, Apache fink, and Twitter's Cascading/Scalding.

关键词： Big Data distributed query processing query Optimization Embedded query Languages

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Efficient scheduling of resources for parallel query processing on grid-based architecture

Efficient scheduling of resources for parallel query process...

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6th Asia-Pacific Symposium on Information and Telecommunication Technologies

作者： Soe, Khin Mar Nwe, Aye Aye Aung, Than Nwe Naing, Thinn Thu Thein, Ni Lar Univ Comp Studies Yangon Myanmar

ISBN: (纸本)4885522161

Parallel query processing is an increasingly attractive option for improving the performance of database systems. It can also be important in Grid since grid technologies have enabled sophisticated interaction and data sharing between resources that may belong to different departments or organizations. The decreasing cost of computing makes parallel query processing economically viable to reduce the response time of decision support queries by using parallel execution to exploit inexpensive resources. In this paper, the architecture of resource scheduling and site selection for parallel queries which are to be executed on the Grid is proposed The main aims are to address the problems of choosing appropriate resources and matching sub plans with these resources. For this reason, decisions have to be made on allocating the available processors among a number of competing database operations running in parallel. For scheduling intra-query parallelism, the new approaches, resource-balancing based site selection algorithm which determines where to execute the upcoming operation sequence based on above architecture, and the resource assignment policy for handling the idle nodes' efficiency well, are proposed. This paper also present the architecture Of scheduling inter-query parallelism and proposed hierarchical model in order to fully exploit the available parallelism and interactions among the different queries.

关键词： grid computing parallel query processing distributed query processing

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distributed In-network processing of k-MaxRS in Wireless Sensor Networks 6

Distributed In-network Processing of <i>k</i>-MaxRS in Wirel...

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6th International Conference on Sensor Networks (SENSORNETS)

作者： Wongse-ammat, Panitan Hussain, Muhammed Mas-ud Trajcevski, Goce Avci, Besim Khokhar, Ashfaq Northwestern Univ Dept Elect Engn & Comp Sci Evanston IL 60208 USA IIT Dept Elect & Comp Engn Chicago IL 60616 USA

ISBN: (纸本)9789897582110

We address the problem of in-network processing of k-Maximizing Range Sum (k-MaxRS) queries in Wireless Sensor Networks (WSN). The traditional, Computational Geometry version of the MaxRS problem considers the setting in which, given a set of (possibly weighted) 2D points, the goal is to determine the optimal location for a given (axes-parallel) rectangle R to be placed so that the sum of the weights (or, a simple count) of the input points in R's interior is maximized. In WSN, this corresponds to finding the location of region R such that the sum of the sensors' readings inside R is maximized. The k-MaxRS problem deals with maximizing the overall sum over k such rectangular regions. Since centralized processing -i.e., transmitting the raw readings and subsequently determining the k-MaxRS in a dedicated sink - incur communication overheads, we devised an efficient distributed algorithm for in-network computation of k-MaxRS. Our experimental observations show that the novel algorithm provides significant energy/communication savings when compared to the centralized approach.

关键词： k-MaxRS Maximizing Range Sum distributed query processing Wireless Sensor Networks

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Array Database Scalability: Intercontinental Queries on Petabyte Datasets 16

Array Database Scalability: Intercontinental Queries on Peta...

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28th International Conference on Scientific and Statistical Database Management (SSDBM)

作者： Dumitru, Alex Mircea Merticariu, Vlad Baumann, Peter Jacobs Univ Bremen Campus Ring 1 D-28757 Bremen Germany

ISBN: (纸本)9781450342155

With the deluge of scientific big data affecting a large variety of research institutions, support for large multidimensional arrays has gained traction in the database community in the past decade. Array databases aim to cover the gap left by traditional relational database systems in the domains of large scientific data by enabling researchers to efficiently store and process their data through rich declarative query languages. Such large amounts of data need effective systems that are able to distribute the processing at both local level, through exploitation of heterogeneous hardware as well as at network level, enabling both intra-cloud and intra-federation distribution of data and processing. In this demonstration we aim to showcase the capabilities of rasdaman by allowing users to execute queries that combine petabyte datasets stored at two institutions on different continents.

关键词： Array databases rasdaman scientific databases heterogeneous-hardware exploitation cloud parallelization inter-federation parallelization distributed query processing

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distributed query Plan Generation Using HBMO

Distributed Query Plan Generation Using HBMO

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7th Multi-Disciplinary International Workshop on Artificial Intelligence (MIWAI)

作者： Kumar, T. V. Vijay Arun, Biri Kumar, Lokendra Jawaharlal Nehru Univ Sch Comp & Syst Sci New Delhi 110067 India

ISBN: (纸本)9783642449499;9783642449482

processing a distributed query entails accessing data from multiple sites. The inter site communication cost, being the dominant cost, needs to be reduced in order to improve the query response time. This would require the query optimizer to devise a distributed query processing strategy that would, for a given distributed query, generate query plans involving fewer number of sites in order to reduce the inter site communication cost. In this paper, a distributed query plan generation algorithm, based on the honey bee mating optimization (HBMO) technique that generates query plans for a distributed query involving less number of sites and higher relation concentration in the participating sites, is presented. Further, experimental comparison of the proposed HBMO based DQPG algorithm with the GA based DQPG algorithm shows that the former is able to generate distributed query plans at a comparatively lesser total query processing cost, which in turn would lead to efficient processing of a distributed query.

关键词： distributed query processing Swarm Intelligence Honey Bee Mating Optimization

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KGRAM Versatile Inference and query Engine for the Web of Linked Data

KGRAM Versatile Inference and Query Engine for the Web of Li...

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11th IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)

作者： Corby, Olivier Gaignard, Alban Zucker, Catherine Faron Montagnat, Johan Univ Nice Sophia Antipolis CNRS Sophia Antipolis Mediterranee INRIA Sophia Antipolis France Univ Nice Sophia Antipolis CNRS Lab I3S Sophia Antipolis France

ISBN: (纸本)9780769548807;9781467360579

querying and linking distributed and heterogeneous databases is increasingly needed, as plentiful data resources are published over the Web. This work describes the design of a versatile query system named KGRAM that supports (i) multiple query languages among which the SPARQL 1.1 standard, (ii) federation of multiple heterogeneous and distributed data sources, and (iii) adaptability to various data manipulation use cases. KGRAM provides abstractions for both the query language and the data model, thus delivering unifying reasoning mechanisms. It is implemented as a modular software suite to ease architecting and deploying dedicated data manipulation platforms. Its design integrates optimization concerns to deliver high query performance. Both KGRAM's software versatility and performance are evaluated.

关键词： Knowledge graphs Data Mediation distributed query processing Modular architecture

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Parallel and distributed Intra query Transient Fault Tolerance Model via Parity Checking 14

Parallel and Distributed Intra Query Transient Fault Toleran...

引用

14th International Conference on Electronics Computer and Computation (ICECCO)

作者： Yusuf, Salisu Ibrahim Junaidu, Sahalu B. Nile Univ Nigeria Dept Comp Sci Abuja Nigeria Ahmadu Bello Univ Dept Comp Sci Abuja Nigeria

ISBN: (纸本)9781728101323

In a distributed parallel query execution, complex queries are executed by splitting them into partially related simple subqueries executing each on a different node, communication between machines is often done by message exchange for shared-nothing architecture based grid. Integrity of messages can be lost by temporary or permanent interference along the communication network. Fault tolerance strategies are used to keep the system running in the presence of fault. This is traditionally done through query restart, replication or check pointing and other variations of these approaches to improve latency, restoration time and reduce cost of execution. These processes include: monitoring, detection and tolerance. Transient faults are caused by interference in the medium of exchange which may pass undetected but yielding an incorrect query result. Moreover, the traditional fault tolerance, there is a strong dependency between the nodes. In this research we proposed a model of a fault tolerance strategy that will allow self-detection and tolerate transient fault with less dependency between nodes. The model will be compared with the tradition strategies in terms of detection ability, inter node dependency, and cost of execution.

关键词： distributed query processing intra-query fault tolerance transient fault shared nothing parallel query execution

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Microsoft SQL Server Parallel Data Warehouse: Architecture Overview

Microsoft SQL Server Parallel Data Warehouse: Architecture O...

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5th BIRTE International Workshop Held at the 37th International Conference on Very Large Databases (VLDB)

作者： Blakeley, Jose A. Dyke, Paul A. Galindo-Legaria, Cesar James, Nicole Kleinerman, Christian Peebles, Matt Tkachuk, Richard Washington, Vaughn Microsoft Corp Aliso Viejo CA 92656 USA

ISBN: (纸本)9783642335006;9783642334993

This paper presents an architecture overview of the Microsoft SQL Server Parallel Data Warehouse (PDW) DBMS system. PDW is a massively parallel processing (MPP), share nothing, scale-out version of SQL Server focused on data warehousing workloads. The product is packaged as a database appliance built on industry standard hardware. We present an overview of the main software and hardware components of the system and an example illustrating its query processing approach.

关键词： distributed database systems data warehousing massively parallel processing database appliance distributed query processing

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Rack-Scale In-Memory Join processing using RDMA 15

Rack-Scale In-Memory Join Processing using RDMA

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ACM SIGMOD International Conference on Management of Data

作者： Barthels, Claude Loesing, Simon Alonso, Gustavo Kossmann, Donald Swiss Fed Inst Technol Dept Comp Sci Syst Grp Zurich Switzerland

ISBN: (纸本)9781450327589

Database systems running on a cluster of machines, i.e. rack-scale databases, are a common architecture for many large databases and data appliances. As the data movement across machines is often a significant bottleneck, these systems typically use a low-latency, high-throughput network such as InfiniBand. To achieve the necessary performance, parallel join algorithms must take advantage of the primitives provided by the network to speed up data transfer. In this paper we focus on implementing parallel in-memory joins using Remote Direct Memory Access (RDMA), a communication mechanism to transfer data directly into the memory of a remote machine. The results of this paper are, to our knowledge, the first detailed analysis of parallel hash joins using RDMA. TO capture their behavior independently of the network characteristics, we develop an analytical model and test our implementation on two different types of networks. The experimental results show that the model is accurate and the resulting distributed join exhibits good performance.

关键词： distributed join rack scale databases join processing with rdma distributed query processing

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