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检索条件"任意字段=International Conference on Big Data Intelligence and Computing, DataCom 2022"

共 1364 条记录，以下是1331-1340 订阅

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A Resource Mapping Method in Cloud-based Satellite Ground System

A Resource Mapping Method in Cloud-based Satellite Ground Sy...

引用

IEEE international conference on Smart City

作者： Cunqun Fan Xiangang Zhao Lizi Xie You Ma Yunpeng Zhang Xiaohu Feng National Satellite Meteorological Center China Meteorological Administration

ISBN: (纸本)9781509018949

The cloud-based computing platform of FY-meteorological satellite ground application system carried more and more tasks, which need to improve the efficiency of tasking virtual resource utilization within the limited system resources. When resource mapping mission, which not only need to consider the priority of the task, but also consider the relationship between resource needs and allocated resources. According to these characteristics requirements, this paper proposed a resource mapping method based on multi-priority coefficient. First, the definition of task weights were given, and the definition of task queue priority factors were given according to the task queuing time. Then, the correlation coefficient method was introduced to calculate the correlation coefficient between tasks required for processing resources and resource pool that can be allocated resources. Finally, task comprehensive priority measure were calculated according to the conditions of multi-resource mapping and the multipriority factors. Experimental results show that the proposed method can improve the cloud system resource utilization, and can meet the timely needs of multi-tasking.

关键词： Satellite ground application system Cloud computing Resource mapping Multipriority factors Correlation coefficient

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Multi-level DC Power Distribution Interface for data Centers

Multi-level DC Power Distribution Interface for Data Centers

引用

IEEE international conference on Smart City

作者： Ding-Feng Huang Hurng-Liahng Jou Jinn-Chang Wu Kuen-Der Wu Jun-Jie Huang National Kaohsiung University of Applied Sciences National Kaohsiung Marine University

ISBN: (纸本)9781509018949

A DC power distribution interface for data centers is developed in this paper. This DC power distribution interface is composed of two power stages, a three-phase T-type multi-level AC-DC power converter and an isolated multi-level DC-DC power converter. The first power stage, three-phase T-type multi-level AC-DC power converter, is used to convert the utility AC voltage to a constant DC voltage and control the utility current to be sinusoidal and unity power factor. The isolated multi-level DC-DC power converter is the second power stage, and it converts the output DC voltage of the first power stage to the desired DC voltage of the DC power distribution system with galvanic isolation. This isolated multi-level DC-DC power converter consists of an isolated power converter with a three-winding transformer, a series/parallel switching circuit and an output L-C filter. Only three power electronics switches are used in the isolated multi-level DC-DC power converter to generate a two-level output DC voltage. Hence, the control circuit is simplified and the capacity of output L-C filter is reduced. Computer simulation is carried out to verify the developed DC power distribution interface. The simulation results show that the developed DC power distribution interface has the expected performance.

关键词： DC power distribution data center Multi-level DC-DC power converter

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SpaceViz:Visualization Tool for the Computer Storage big-data

SpaceViz:Visualization Tool for the Computer Storage Big-dat...

引用

IEEE international conference on Smart City

作者： J. Joshua Thomas Ahamad Tajudin Khader Bahari Belaton Department of Computing School of Engineering Science and Technology KDU University College Penang School of Computer Sciences University Sains Malaysia

ISBN: (纸本)9781509018949

This paper presents SpaceViz, an information visualization tool designed for depicting hierarchies, such as directory structures. SpaceViz has scanned, map and manage PC, network and cloud storage of multimedia data. Classical, space-filling squarified techniques are used as methodology, but differ from Tree-map single view in that it employs multiple views in two-dimensional space, which is used to more clearly the structural relationships of data from colour code and pattern based hierarchy. The paper includes an empirical study comparing SpaceViz and Tree-map. The study shows the participants perform significantly better to interact with multiple file formats using SpaceViz.

关键词： Information Visualization Tree-maps Computer Storage big-data

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An Inter-Framework Cache for Diverse data-Intensive computing Environments

An Inter-Framework Cache for Diverse Data-Intensive Computin...

引用

IEEE international conference on Smart City

作者： Chun-Yu Wang Tzu-En Huang Yu-Tang Huang Jyh-Biau Chang Ce-Kuen Shieh Institution of Computer and Communication Engineering Department of Electrical Engineering National Cheng Kung University Department of Digital Applications University of Kang Ning

ISBN: (纸本)9781509018949

Hadoop Distributed File System (HDFS) provides the storage to keep analyzing outcomes for the diversity of frameworks. MapReduce, Storm, and Spark each applies on batching, streaming and in-memory computing, all of them need the HDFS to collect and assemble results. For coping with big-data analysis in the real world, complicated platforms required working together. However, collaborating analysis on heterogeneous frameworks, the data must be write-through firstly and post-fetch upon HDFS that degrades the performance and lower the effectiveness of the whole system. For best our knowledge, no previous work had focused on inter-framework data caching. To solve above problems on collaborating analysis within heterogeneous frameworks such as Hadoop and Strom, in this paper, we propose a cache system upon YARN called " Inter-Framework Cache" (IF-cache). It uses in-memory cache to reserve temporary outcomes while also reducing the HDFS access frequency and improve analysis performance. Experiments had shown that Hadoop with IF-cache can reduce about 50% times comparing the no-cache one.

关键词： MapReduce Hadoop YARN big data Cache mechanism

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A general accelerated R package using GPU

A general accelerated R package using GPU

引用

IEEE international conference on Smart City

作者： Jie Huang Bojin Zhuang Fei Su School of Information and Communication Engineering Beijing University of Posts and Telecommunications

ISBN: (纸本)9781509018949

In this paper, we present cuLib, a R package that provides an easy-to-access interface for utilizing the computing power of NVIDIA GPU. The cuLib package aims to make GPU-based parallel programming easier, flexible and high-performance. It allows the use of GPU computing in R without further knowledge because the syntax for definition and manipulation of GPU data is similar to formal R language. cuLib is compatible with device and operation system. The data interface is very flexible, enabling the users manipulate data freely. The cuLib package provides an R wrapper for libraries of NVIDIA's CUDA toolkit and numerous operations. More importantly, it is not only a mathematical tool but also practicable for the algorithm in dealing with data intensive computation.

关键词： R GPU CUDA Mathematical tool data intensive computing

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Volatility Analysis of Chinese Stock Market Using High-Frequency Financial big data

Volatility Analysis of Chinese Stock Market Using High-Frequ...

引用

IEEE international conference on Smart City

作者： Tongtong Dong Bowei Yang Tianhai Tian School of Statistics and Mathematics Zhongnan University of Economics and Law School of Mathematical Sciences Monash University

ISBN: (纸本)9781509018949

With the recent development of computer technology, the high frequency financial big data have been generated timely and more conveniently. However, the particularity of high-frequency big data has raised a number of major challenges for data analysis. The existing mathematical models that were designed for analyzing daily financial data may no longer be suitable for studying high-frequency big data. To tackle this challenge, this work explores the appropriate model that is able to analyze the high-frequency financial big data from the Shanghai composite index. For analyzing market volatility, we conduct three comparison studies for different mathematical models. We first compare the effect of two types GARCH (generalized autoregressive conditional heteroskedasticity) models. Numerical results suggest that the volatility proxy model has a better effect than the model based on the return of Shanghai composite index. This study leads to the comparison study of the GARCH(1,1) model and GJR(1,1) (Glosten-Jagannathan-Runkle) model. The result show that the GJR(1,1) model is more efficient than the GARCH(1,1) model. Finally we introduce the ARMA model based on the GJR volatility proxy model. Analysis results indicate that the ARMA(2,1)-GJR volatility proxy model is the most effective one to study market volatility. The volatility persistence parameter is 0.952, which is very close to 1. In addition, the p-value of the Ljung-Box test is 0.729, which suggests that this model can not only correct the problem of residual but also reflect the leverage effect and long memory character of the Chinese stock market.

关键词： High frequency data GARCH Volatility proxy model GJR

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An Elastic data Persisting Solution with High Performance for Spark

An Elastic Data Persisting Solution with High Performance fo...

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IEEE international conference on Smart City

作者： Zhipeng Jiang Haopeng Chen Huan Zhou Jenny Wu School of Software Shanghai Jiao Tong University Data Center Group Intel Asia-Pacific R&D Ltd. Research and Development Cornerstone Research

ISBN: (纸本)9781509018949

With the increasing popularity of in-memory computing, Spark [1] has been highly successful in implementing large scale data intensive applications, especially for those that reuse data across multiple parallel operations. However due to the fact that Moore's Law has slowed down and memory resources are still costly, we presented an elastic data persisting solution for Spark, which enables data compression to save more heap space for JVM and reducing disk I/O throughput for faster data access. We mathematically derived the criteria for selecting the optimal data compression and persisting plan. Our evaluation of the preliminary prototype of this elastic data persisting solution shows that it can provide resource management recommendations by accounting for input data type, memory space and CPU resource, and can consistently yield high performance that accelerates Spark up to 6×.

关键词： Spark In-memory computing data Compression data Persisting data Caching

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A topic-level privacy preserving search in the medical field

A topic-level privacy preserving search in the medical field

引用

IEEE international conference on Smart City

作者： Meng Tian Jianqiang Li Xi Meng Rong Li Jing Bi Juan Li Yu Zhao Bo Liu School of Software Engineering Beijing University of Technology People's Public Security University of China NEC Labs

ISBN: (纸本)9781509018949

With the explosive growth of medical information, the users not only query information efficiently and accurately, but also pay attention to the information of sensitivity and privacy. In medical domains, the current privacy preserving methods either use the technology of Access Control List, or need to prepare training documents for each privacy policy. However, it is a time-consuming and impractical way for data owners to assign a privacy policy on each document. In this paper, by exploiting the privacy medical queries, we propose a novel approach based on semantic and ontology to achieve the topic-level privacy preserving search. With the support of them, we first mine all the potential hierarchy and semantics from a user query and acquire sensitive terms relative to privacy policies automatically without training documents.

关键词： Privacy and access control for cloud computing Medical information retrieval Ontology LSA

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Intelligent Systems and Sustainable computing 1

引用

丛书名： Smart Innovation, Systems and Technologies

1000年

作者： V. Sivakumar Reddy V. Kamakshi Prasad Jiacun Wang Naga Mallikarjuna Rao Dasari

ISBN: (数字)9789819947171

ISBN: (纸本)9789819947164;9789819947195

This book is a collection of best selected research papers presented at Second international conference on Intelligent Systems and Sustainable computing (ICISSC 2022), held in School of Engineering, Malla Reddy University, Hyderabad, India, during December 16–17, 2022. The book covers recent research in intelligent systems, intelligent business systems, soft computing, swarm intelligence, artificial intelligence and neural networks, data mining and data warehousing, cloud computing, distributed computing, big data analytics, Internet of things (IoT), machine learning, speech processing, sustainable high-performance systems, VLSI and embedded systems, image and video processing and signal processing and communication.

关键词： Computational intelligence Artificial intelligence Cyber-physical systems, IoT Professional computing Statistics, general Signal, Image and Speech Processing

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Marketing and Smart Technologies 1

引用

丛书名： Smart Innovation, Systems and Technologies

1000年

作者： José Luís Reis Marisa Del Rio Araujo Luís Paulo Reis José Paulo Marques dos Santos

ISBN: (数字)9789819903337

ISBN: (纸本)9789819903320;9789819903351

This book includes selected papers presented at the international conference on Marketing and Technologies (ICMarkTech 2022), held at Universidade de Santiago de Compostela, Spain, during 1 – 3 December 2022. It covers up-to-date cutting-edge research on artificial intelligence applied in marketing, virtual and augmented reality in marketing, business intelligence databases and marketing, data mining and big data, marketing data science, web marketing, e-commerce and v-commerce, social media and networking, geomarketing and IoT, marketing automation and inbound marketing, machine learning applied to marketing, customer data management and CRM, and neuromarketing technologies.

关键词： Cyber-physical systems, IoT Professional computing Artificial intelligence Computational intelligence Statistics, general

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