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检索条件"任意字段=International Conference on Parallel and Distributed Processing Techniques and Applications"

共 11287 条记录，以下是4931-4940 订阅

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Exploring Deep Insights from Vast Data: An Overview of Deep Learning techniques for Big Data 4

Exploring Deep Insights from Vast Data: An Overview of Deep ...

引用

4th international conference on Electrical Engineering and Computer Science, ICECOS 2024

作者： Baez Hasan, Sarhad Kareem, Shahab Wahhab Ameen, Didar Dlshad Hamad Soran University Faculty of Science Computer Science Department Soran Iraq Erbil Polytechnic University College of Engineering Knowledge University Department of Information System Engineering Department of Computer Engineering Erbil Iraq

ISBN: (纸本)9798350368253

Big data has emerged very fast, and this has brought both opportunities and problems that are related to the application of deep learning. This paper explores how deep learning can be implemented using big data and in particular the Apache Mahout library. Unlike most other machine learning tools, Mahout can perform distributed computation, especially while using the MapReduce frameworks including Apache Hadoop and Apache Spark. However, this study does not only focus on the explanation of the need for Mahout's integration and employment but also carries out a high-fidelity argument on how deep learning algorithms can be incorporated in the Mahout framework to optimize the manipulation of large and complicated datasets. Mahout-based contributions include the comparative analysis of key deep learning paradigms including distributed processing, parallel processing and transfer learning. This paper also includes examples of the Mahout deep learning integration use and its application in real contexts. This article describes the challenges that currently organizations face in handling big data and indeed, the potential threats these challenges pose to the success of deep learning techniques when used in organizations' big data processing and analysis. © 2024 IEEE.

关键词： Adversarial machine learning

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A New distributed Grid Scheme Utilizing Node-based Preprocessing Technique for Supporting k-NN Queries in Location-based Services 1

引用

2nd FTRA international conference on Ubiquitous Computing Application and Wireless Sensor Network (UCAWSN)

作者： Lee, Hyunjo Yoon, Min Chang, Jae-Woo Chonbuk Natl Univ Dept Comp Engn Chonju Chonbuk 56756 South Korea

ISBN: (数字)9789401796187

ISBN: (纸本)9789401796187;9789401796170

Because moving objects usually move on spatial networks in location-based service applications, their locations are updated frequently, leading to the degradation of retrieval performance. To manage the frequent updates of moving objects' locations in an efficient way, we propose a new distributed grid scheme which utilizes node-based pre-computation technique to minimize the update cost of the moving objects' locations. Because our grid scheme manages spatial network data separately from the POIs (Point of Interests) and moving objects, it can minimize the update cost of the POIs and moving objects. Using our grid scheme, we propose a new k-nearest neighbor (k-NN) query processing algorithm which minimizes the number of accesses to adjacent cells during POIs retrieval in a parallel way. Finally, we show from our performance analysis that our k-NN query processing algorithm is better on retrieval performance than that of the existing S-GRID.

关键词： distributed grid scheme Query processing algorithm Road network Moving objects

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parallel contour matching and 3D partial retrieval in bio-image database

Parallel contour matching and 3D partial retrieval in bio-im...

引用

21st international conference on Advanced Information Networking and applicationsWorkshops/Symposia, AINAW'07

作者： Li, Yong Chen, Xiujuan Belkasim, Saeid Pan, Yi Department of Computer Science Georgia State University P.O. Box 3994 Atlanta GA 30302-3994 United States

ISBN: (纸本)0769528473

parallel 3D reconstruction and partial retrieval from 2D image slices based on object contour structure are introduced. In this paper, 2D images are segmented into object contours. Related object contours on adjacent image slices are linked together to form the 3D component. parallel algorithms are designed and implemented for distributing contour matching and partial retrieval tasks among multiple processors. The methods have been implemented in MPI on an SGI Origin 2000 machine. The experimental results show good speedups for sequential object matching and partial 3D component retrieval. © 2007 IEEE.

关键词： Image reconstruction

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Using OpenDX to Teach the Concept of Visualization Pipeline 19

Using OpenDX to Teach the Concept of Visualization Pipeline

引用

第十五届分布式计算及其应用国际学术研讨会

作者： Anna Perduta Roman Putanowicz Institute for Computational Civil Engineering Cracow University of Technology

ISBN: (纸本)9781509035946

Visualization of experimental data and results of numerical simulations belongs to most basic skills that need to be mastered by students at undergraduate,graduate and postgraduate *** faced with many visualization techniques and available tools,deep understanding of the fundamental concepts,such as visualization pipeline,is the necessary foundation for using data visualization *** article shows how concept of pipeline processing can be explained to students and how the tool such as OpenDX,with its visual programming capabilities,can be used to teach basic visualization concepts and techniques.

关键词： Data visualization Pipeline processing Tools Visualization Graphical user interfaces Solid modeling Pipelines

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NEMICO: Mining network data through cloud-based data mining techniques 14

NEMICO: Mining network data through cloud-based data mining ...

引用

IEEE/ACM 7th international conference on Utility and Cloud Computing (UCC)

作者： Baralis, Elena Cagliero, Luca Cerquitelli, Tania Chiusano, Silvia Garza, Paolo Grimaudo, Luigi Pulvirenti, Fabio Politecn Torino Dipartimento Automat & Informat Turin Italy

ISBN: (纸本)9781479978816

Thanks to the rapid advances in Internet-based applications, data acquisition and storage technologies, petabyte-siwzed network data collections are becoming more and more common, thus prompting the need for scalable data analysis solutions. By leveraging today's ubiquitous many-core computer architectures and the increasingly popular cloud computing paradigm, the applicability of data mining algorithms to these large volumes of network data can be scaled up to gain interesting insights. This paper proposes NEMICO, a comprehensive Big Data mining system targeted to network traffic flow analyses (e.g., traffic flow characterization, anomaly detection, multiple-level pattern mining). NEMICO comprises new approaches that contribute to a paradigm-shift in distributed data mining by addressing most challenging issues related to Big Data, such as data sparsity, horizontal scaling, and parallel computation.

关键词： Cloud Computing

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Vector Field Visualization Review and Prospects

Vector Field Visualization Review and Prospects

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international conference on Virtual Reality and Visualization (ICVRV)

作者： Li Chao Wu Lingda Acad Equipment Beijing 101416 Peoples R China

ISBN: (纸本)9781479968541

Vector field visualization is an important aid to help user understand data of complex spatial features in scientific and engineering applications. This paper summarizes relative researches on vector field visualization completely and systematically, gives an induction on major techniques, compares their advantages and shortcomings, and introduces some representative related or cross-over studies. In the end, according to current research progress, this paper looks to the future of improvement and development of vector field visualization.

关键词： Vector field visualization Texture-based visualization Geometric visualization Direct visualization parallel algorithms

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QoS-Aware and Fault-Tolerant Replica Placement 20th

QoS-Aware and Fault-Tolerant Replica Placement

引用

20th international conference on Algorithms and Architectures for parallel processing (ICA3PP)

作者： Hu, Jingkun Du, Zhihui Zhang, Sen Bader, David A. Worldmoney Blockchain Management Ltd Hong Kong Peoples R China New Jersey Inst Technol Newark NJ 07102 USA SUNY Coll Oneonta Oneonta NY 13820 USA

ISBN: (纸本)9783030602390;9783030602383

As emerging applications become more and more distributed and decentralized, it has become a more challenging problem to design and build fault-tolerant network systems with high Quality of Service (QoS) guarantee. In this paper, an optimal replica placement problem is formulated in terms of minimizing the replica placement cost subject to both QoS and fault-tolerant constraints. Based on the generalized graph model, the optimal replica placement problem is proved to be NP-hard. The essential properties of the proposed problem are investigated and two criteria, which can quantitatively measure the replica demand of each node and the contribution of one replica to other nodes in the graph, are proposed. The criteria are employed to develop efficient heuristic algorithms. Finally the proposed algorithms are evaluated with extensive network configurations and the experimental results show that the proposed heuristic algorithms can generate solutions very close to the optimal results.

关键词： Replica placement Quality of service Fault tolerance Heuristic algorithm distributed system

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STGE: Sensor Topology and Graph Embedding Learning with Heterogeneous Smart Environment 27

STGE: Sensor Topology and Graph Embedding Learning with Hete...

引用

27th IEEE international conference on parallel and distributed Systems, ICPADS 2021

作者： Wang, Xiao Zhang, Sheng Pan, Yi Lin, Zeyu Shenzhen International Graduate School Tsinghua University China Tsinghua University Department of Electronic Engineering China

ISBN: (纸本)9781665408783

In recent years, the Smart Environment(SE) supported by the Internet of Things(IoT) shows great ability to acquire and apply knowledge about an environment. Typical tasks in such environments are intended to monitor and predict the behavior patterns of residents. It would be helpful to these tasks if we can model the rich multi-model perceptional context and select the right combinations of sensory resources. In this study, we propose STGE, a novel sensor topology learning and graph embedding technique. STGE consists of two phases. 'ST' applies data-driven adjacency learning methods to acquire sensor logical topology, which helps to determine the combination of sensors. 'G E' learns representations of sensory resources based on the sensor topology, and leverages ideas of a novel graph embedding model called struct2vec. Through experiments with Smart Home(SH) data-sets, we demonstrate that STGE can achieve explainable and computable sensor's logical topology and their latent representation across different smart homes. The learned embedding is compatible with machine learning techniques, and can optimize the performance of downstream tasks such as human activity recognition. © 2021 IEEE.

关键词： Topology

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Performance analysis of applying replica selection technology for Data Grid environments

Performance analysis of applying replica selection technolog...

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8th international conference on parallel Computing Technologies, PaCT 2005

作者： Yang, Chao-Tung Chen, Chun-Hsiang Li, Kuan-Ching Hsu, Ching-Hsien High-Performance Computing Laboratory Department of Computer Science and Information Engineering Tunghai University Taichung 40704 Taiwan Parallel and Distributed Processing Center Department of Computer Science and Information Management Providence University Taichung 43301 Taiwan Department of Computer Science and Information Engineering Chung Hua University Hsinchu 300 Taiwan

The Data Grid enables the sharing, selection, and connection of a wide variety of geographically distributed computational and storage resources for solving large-scale data intensive scientific applications. Such technology efficiently manage and transfer terabytes or even petabytes of data for data-intensive, high-performance computing applications in wide-area, distributed computing environments. Replica selection process allows an application to choose a replica from replica catalog, based on its performance and data access features. In this paper, we build a Grid environment based on three existing PC Cluster environments and perform performance analysis of data transfers using GridFTP protocol over these systems. In addition, based on experimental results, it is proposed a cost model to pick the best replica, in real and dynamic network situations. © Springer-Verlag Berlin Heidelberg 2005.

关键词： distributed computer systems

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Accelerate SOMA Using parallel processing in GPGPU

Accelerate SOMA Using Parallel Processing in GPGPU

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international conference on Advanced Engineering Theory and applications (AETA)

作者： Tran Trong Dao Nguyen Mau Toan Vo Hoang Duy Zelinka, Ivan Ton Duc Thang Univ Fac Elect & Elect Engn MERLIN Ho Chi Minh City Vietnam VSB Tech Univ Ostrava Dept Comp Sci 17 Listopadu 15-2172 Ostrava 70833 Czech Republic

ISBN: (纸本)9783319509044;9783319509037

This paper presents methods for implementing SOMA (Self-Organizing Migrating Algorithm) in parallel with the CUDA (Compute Unified Device Architecture) system that can be used to perform the dominant of up-speed when using SOMA algorithm. SOMA has many individual points to find the global minimum which is the key for paralleling this system because each individual can work separately and share the position for all when it moves. Nowadays, due to the humongous size of data and the limitation of the process in single Central processing Unit (CPU), it becomes impossible to deal with. As a result of these limitations, we need more CPUs working at the same time to do the same job or take advantage of the power of parallel processing in GPGPU (General-Purpose graphics processing unit). Additionally, many supercomputers are built with the need of parallel processing in order to meet the power of hardware. Based on the architecture of CUDA, it can handle the threads in SOMA independence. We use two methods with different architecture in CUDA to help SOMA run much faster than single threading method. This paper also uses some techniques to help SOMA work more effective.

关键词： Simulation Optimization Evolutionary algorithms Self-organizing migrating algorithms CUDA parallel processing GPGPU

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