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26th International Conference on parallel and distributed Computing (Euro-Par)

作者： Lee, Ming-Chang Lin, Jia-Chun Gran, Ernst Gunnar Norwegian Univ Sci & Technol Dept Informat Secur & Commun Technol N-2815 Gjovik Norway Simula Res Lab N-1364 Fornebu Norway

ISBN: (纸本)9783030576752;9783030576745

Short-term traffic speed prediction has been an important research topic in the past decade, and many approaches have been introduced. However, providing fine-grained, accurate, and efficient traffic-speed prediction for large-scale transportation networks where numerous traffic detectors are deployed has not been well studied. In this paper, we propose DistPre, which is a distributed fine-grained traffic speed prediction scheme for large-scale transportation networks. To achieve fine-grained and accurate traffic-speed prediction, DistPre customizes a Long Short-Term Memory (LSTM) model with an appropriate hyperparameter configuration for a detector. To make such a customization process efficient and applicable for large-scale transportation networks, DistPre conducts LSTM customization on a cluster of computation nodes and allows any trained LSTM model to be shared between different detectors. If a detector observes a similar traffic pattern to another one, DistPre directly shares the existing LSTM model between the two detectors rather than customizing an LSTM model per detector. Experiments based on traffic data collected from freeway I5-N in California are conducted to evaluate the performance of DistPre. The results demonstrate that DistPre provides time-efficient LSTM customization and accurate fine-grained traffic-speed prediction for large-scale transportation networks.

关键词： Hyperparameter tuning Lightweight LSTM Large-scale transportation networks Traffic speed prediction distributed and parallel processing The Nelder-Mead method

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Enabling Interactive Visualizations in Industrial Big Data 21st

Enabling Interactive Visualizations in Industrial Big Data

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21st IFAC World Congress on Automatic Control - Meeting Societal Challenges

作者： Bezerra, Aguinaldo Greati, Vitor Campos, Vinicius Silva, Ivanovitch Guedes, Luiz A. Leitao, Gustavo Silva, Diego Univ Fed Rio Grande do Norte Natal RN Brazil

Industries are considered data rich but information poor environments. Mainly due to systems design restrictions, to the lack of adequate processing power and to a sector culture notably focused on collecting, selecting, storing and preserving historical series in on-demand access repositories, massive data generated in industrial operations is traditionally neglected (or simply took aside). This huge amount of unprocessed data resting in these repositories is a latent and rich source of information that could be used to improve industrial processes. This work then proposes an approach in which an elastic processing engine is designed to be plugged-in to currently installed industrial information infrastructure to provide it with the ability of performing visual analytics on massive industrial data. A case study where an interactive visualization application is made possible in real-world industrial data scenario of over 100 million records is presented to attest the effectiveness and potential of the proposed approach in enabling interactive visualizations to Industrial Big Data. Copyright (C) 2020 The Authors.

关键词： Knowledge discover (data mining) intelligent decision support systems in manufacturing industrial Big Data visual analytics distributed and parallel processing

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A distributed data management system to support large-scale data analysis

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JOURNAL OF SYSTEMS AND SOFTWARE 2019年 148卷 105-115页

作者： Emara, Tamer Z. Huang, Joshua Zhexue Shenzhen Univ Coll Comp Sci & Software Engn Big Data Inst Shenzhen 518060 Guangdong Peoples R China Shenzhen Univ Natl Engn Lab Big Data Syst Comp Technol Shenzhen 518060 Guangdong Peoples R China

distributed data management is a key technology to enable efficient massive data processing and analysis in cluster-computing environments. Specifically, in environments where the data volumes are beyond the system capabilities, big data files are required to be summarized by representative samples with the same statistical properties as the whole dataset. This paper proposes a big data management system (BDMS) based on distributed random sample data blocks. It presents a high-level architecture design of the BDMS which extends the current distributed file systems. This system offers certain functionalities for block-level management such as statistically-aware data partitioning, data blocks organization, and data blocks selection. This paper also presents a round-random partitioning scheme to represent a big dataset as a set of non-overlapping data blocks;each block is a random sample of the whole dataset. Based on the presented scheme, two algorithms are introduced as an implementation strategy to convert the HDFS blocks of a big file into a set of random sample data blocks which is also stored in HDFS. The experimental results show that the execution time of partitioning operation is acceptable in the real applications because this operation is only performed once on each input data file. (C) 2018 Elsevier Inc. All rights reserved.

关键词： Big data distributed and parallel processing Random sample partition Randomness Data management

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Adaptive Information Visualization for Maritime Traffic Stream Sensor Data with parallel Context Acquisition and Machine Learning

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SENSORS 2019年第23期19卷 5273-5273页

作者： Kim, Kwang-il Lee, Keon Myung Jeju Natl Univ Dept Marine Ind & Maritime Police Jeju 64343 South Korea Chungbuk Natl Univ Dept Comp Sci Cheongju 28644 South Korea

Excessive information significantly increases the mental burden on operators of critical monitoring services such as maritime and air traffic control. In these fields, vessels and aircraft have sensors that transmit data to a control center. Because of the large volume of collected data, it is infeasible for monitoring stations to display all of the information on monitoring screens that have limited sizes. This paper proposes a method for automatically selecting maritime traffic stream data for display from a large number of candidates in a context-aware manner. Safety is the most important concern in maritime traffic control, and special care must be taken to avoid collisions between vessels at sea. It presents an architecture for an adaptive information visualization system for a maritime traffic control service. The proposed system adaptively determines the information to be displayed based on the safety evaluation scores and expertise of vessel traffic service operators. It also introduces a method for safety context acquisition to assess the risk of collisions between vessels, using parallel and distributed processing of maritime stream data transmitted by sensors on the vessels at sea. It provides an information-filtering, knowledge extraction method based on the work logs of traffic service operators, using a machine learning technique to generate a decision tree. We applied the proposed system architecture to a large dataset collected at a port. Our results indicate that the proposed system can adaptively select traffic information according to port conditions and to ensure safety and efficiency.

关键词： big data stream data context-aware service distributed and parallel processing vessel traffic service maritime traffic stream sensor data

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RIDE: real-time massive image processing platform on distributed environment

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EURASIP JOURNAL ON IMAGE AND VIDEO processing 2018年第1期2018卷 1页

作者： Kim, Yoon-Ki Kim, Yongsung Jeong, Chang-Sung Korea Univ Dept Elect Engn Seoul South Korea

As the demand for real-time data processing increases, a high-speed processing platform for large-scale stream data becomes necessary. For fast processing large-scale stream data, it is essential to use multiple distributed nodes. So far, there have been few studies on real-time massive image processing through efficient management and allocation of heterogeneous resources for various user-specified nodes on distributed environments. In this paper, we shall present a new platform called RIDE (Real-time massive Image processing platform on distributed Environment) which efficiently allocates resources and executes load balancing according to the amount of stream data on distributed environments. It minimizes communication overhead by using a parallel processing strategy which handles the stream data considering both coarse-grained and fine-grained parallelism simultaneously. Coarse-grained parallelism is achieved by the automatic allocation of input streams onto partitions of broker buffer each processed by its corresponding worker node, and maximized by adaptive resource management which adjusts the number of worker nodes in a group according to the frame rate in real time. Fine-grained parallelism is achieved by parallel processing of task on each worker node and maximized by allocating heterogeneous resources such as GPU and embedded machines appropriately. Moreover, it provides a scheme of application topology which has a great advantage for higher performance by configuring the worker nodes of each stage using adaptive heterogeneous resource management. Finally, it supports dynamic fault tolerance for real-time image processing through the coordination between components in our system.

关键词： Real-time Image processing distributed and parallel processing Heterogeneous computing

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Empirical Analysis of Asymptotic Ensemble Learning for Big Data 16

Empirical Analysis of Asymptotic Ensemble Learning for Big D...

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3rd IEEE/ACM International Conference on Big Data Computing Applications and Technologies (BDCAT)

作者： Salloum, Salman Huang, Joshua Zhexue He, Yulin Shenzhen Univ Coll Comp Sci & Software Engn Big Data Inst Shenzhen 518060 Guangdong Peoples R China

ISBN: (纸本)9781450346177

In many application areas, data that is being generated and processed goes beyond the petabyte scale. Analyzing such an increasing massive volume of data faces computational, as well as, statistical challenges. In order to solve these challenges, distributed and parallel processing frameworks have been used for implementing scalable data analysis algorithms. Nevertheless, processing the whole big data set at one time may exceed the available computing resources and the time requirements for some applications. Thus, approximate approaches can be used to achieve asymptotic analysis results, especially when data analysis algorithms are amenable to an approximate result rather than an exact one. However, most approximation approaches require taking a random sample of the data which is a nontrivial task when working with big data sets. In this paper, we employ ensemble learning as an approach for asymptotic analysis using randomly selected subsets (i.e. data blocks) of a big data set. We propose an asymptotic ensemble learning framework which depends on block-based sampling rather than record-based sampling. In order to demonstrate the feasibility and performance of this framework, we present an empirical analysis on real data sets. In addition to the scalability advantage, the experimental results show that several blocks of a data set are enough to get approximately the same results as those from using the whole data set.

关键词： Big Data distributed and parallel processing Ensemble Learning Randomness Asymptotic Analysis

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Analysis and Real Implementation of a Cloud Infraestructure for Computing Laboratories Virtualization

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8th International Conference on Image processing and Communications (IP and C)

作者： Enrique Munoz-Exposito, Jose Perez de Prado, Rocio Garcia-Galan, Sebastian Rodriguez-Reche, Rafael Marchewka, Adam Univ Jaen Telecommun Engn Dept Alfonso X El Sabio 28 Linares Spain UTP Univ Sci & Technol Bydgoszcz Inst Telecommun & Comp Sci Bydgoszcz Poland

ISBN: (纸本)9783319238142;9783319238135

In this work, a cloud infrastructure is analysed and deployed to virtualize computing laboratories. Virtualization of computing resources allows the user to face a single computing interface whose capabilities are really offered by a cloud system that makes use of its processing capability on demand. The use of this proposed infrastructure reduces the cost of equipment investments as well as maintenance of real laboratories and further, it makes a more efficient use of available resources with a transparent user experience.

关键词： Computing virtualization Cloud computing OpenNebula distributed and parallel processing

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Complexity Analysis of Multilayer Perceptron Neural Network Embedded into a Wireless Sensor Network

Complexity Analysis of Multilayer Perceptron Neural Network ...

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Conference on Conquering Complexity - Challenges and Opportunities

作者： Serpen, Gursel Gao, Zhenning Univ Toledo Toledo OH 43606 USA

This paper presents computational and message complexity analysis for a multi-layer perceptron neural network, which is implemented in fully distributed and parallel form across a wireless sensor network. Wireless sensor networks offer a promising platform for parallel and distributed neurocomputing as well as potentially benefiting from artificial neural networks for enhancing their adaptation abilities and computational intelligence. Multilayer perceptron (MLP) neural networks are generic function approximators and classifiers with countless domain-specific applications as reported in the literature. Accordingly, embedding a multilayer perceptron neural network in a wireless sensor network in parallel and distributed mode offers synergy and is very promising. Accordingly, assessing the computational and communication complexity of such hybrid designs, namely an artificial neural network such as a multilayer perceptron network embedded within a wireless sensor network, of interest. This paper presents bounds and results of empirical study on the time, space and message complexity aspects of a wireless sensor network and multilayer perceptron neural network design. (C) 2014 The Authors. Published by Elsevier B.V.

关键词： distributed and parallel processing multilayer perceptron neural network wireless sensor network computational complexity communication complexity

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Complexity Analysis of Multilayer Perceptron Neural Network Embedded into a Wireless Sensor Network

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Procedia Computer Science 2014年 36卷 192-197页

作者： Gursel Serpen Zhenning Gao Electrical Engineering and Computer Science University of Toledo Toledo Ohio 43606 USA

关键词： distributed and parallel processing multilayer perceptron neural network wireless sensor network computational complexity communication complexity

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Extending X3D for distributed multimedia processing and control 09

Extending X3D for distributed multimedia processing and cont...

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Proceedings of the 14th International Conference on 3D Web Technology

作者： Michael Repplinger Alexander Löffler Benjamin Schug Philipp Slusallek Saarland University

ISBN: (纸本)9781605584324

Web-based applications of interactive 3D computer graphics are showing a tendency to get more interconnected and visually complex. Virtual communities like Second Life demand realism not only in terms of realistic rendering, but also in terms of integrated multimedia content. For these Web-based applications, X3D is the ISO-standard way to specify and manipulate scene descriptions. In terms of multimedia integration, however, X3D offers to specify content only in the form of URLs pointing to files. Modern middleware for distributed multimedia, on the other hand, allows applications to harness the full range of multimedia processing as well as transparent use and full control of both local and remote components. Integrating a full multimedia processing pipeline into X3D would enable Web authors to use, for example, streaming media, post-processing on media streams, or routing between scene elements (e.g., sensors) and elements of multimedia processing (e.g., TV cards). A full integration of multimedia in X3D is yet *** this paper, we propose X3D extensions for a seamless mapping of a distributed multimedia flow graph onto an X3D scene graph, making all the features of a distributed multimedia middleware accessible and usable within an X3D scene. We present our proposed specification and implementation of multimedia nodes for X3D. Using examples and implemented X3D application scenarios, we show the simplicity and feasibility of our approach.

关键词： X3D multimedia middleware distributed processing distributed flow graph distributed and parallel processing

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