检索结果-内蒙古大学图书馆

您好，读者！请登录

内蒙古大学图书馆

首页
概况
党建
资源
服务
科研支持
- 论文收录引用证明
- 科技查新
知识产权
档案馆
帮助

咨询与建议

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

您的常用邮箱：*

您的手机号码：*

问题描述：

当前已输入0个字，您还可以输入200个字

全部搜索
期刊论文
图书
学位论文
标准
纸本馆藏
外文资源发现
数据库导航
超星发现

高级检索

时间限定

出版年份：

文献类型

图书期刊文献学位论文多媒体

馆藏选择

电子馆藏纸本馆藏

核心期刊

全部期刊 SCI 收录期刊 SSCI 收录期刊 EI 收录期刊 CSCD 收录期刊 CSSCI 收录期刊

语言

中文英文

文献类型

期刊文献图书学位论文标准纸本馆藏

帮助

文字说明：

T=题名（书名、题名），A=作者（责任者），K=主题词，P=出版物名称，PU=出版社名称，O=机构（作者单位、学位授予单位、专利申请人），L=中图分类号，C=学科分类号，U=全部字段，Y=年（出版发行年、学位年度、标准发布年）

检索规则说明：

AND代表“并且”；OR代表“或者”；NOT代表“不包含”；(注意必须大写,运算符两边需空一格)

检索范例：

范例一：(K=图书馆学 OR K=情报学) AND A=范并思 AND Y=1982-2016
范例二：P=计算机应用与软件 AND (U=C++ OR U=Basic) NOT K=Visual AND Y=2011-2016

分类表

所选分类

>> <<

限定检索结果

文献类型

1,699 篇 会议
48 册 图书
25 篇 期刊文献

馆藏范围

1,772 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

1,366 篇 工学
- 1,236 篇 计算机科学与技术...
- 550 篇 软件工程
- 241 篇 信息与通信工程
- 219 篇 电气工程
- 74 篇 控制科学与工程
- 50 篇 电子科学与技术（可...
- 43 篇 生物工程
- 34 篇 机械工程
- 26 篇 动力工程及工程热...
- 22 篇 仪器科学与技术
- 21 篇 力学（可授工学、理...
- 20 篇 建筑学
- 19 篇 土木工程
- 18 篇 生物医学工程（可授...
- 15 篇 材料科学与工程（可...
- 15 篇 安全科学与工程
- 14 篇 化学工程与技术
- 13 篇 石油与天然气工程
329 篇 理学
- 227 篇 数学
- 55 篇 物理学
- 51 篇 生物学
- 29 篇 系统科学
- 28 篇 统计学（可授理学、...
- 20 篇 化学
213 篇 管理学
- 157 篇 管理科学与工程(可...
- 66 篇 图书情报与档案管...
- 49 篇 工商管理
71 篇 医学
- 62 篇 临床医学
15 篇 法学
- 13 篇 社会学
14 篇 经济学
- 14 篇 应用经济学
9 篇 文学
8 篇 教育学
6 篇 农学
1 篇 军事学

主题

109 篇 distributed comp...
79 篇 parallel process...
76 篇 distributed comp...
62 篇 cloud computing
51 篇 parallel computi...
44 篇 computational mo...
41 篇 parallel program...
39 篇 computer archite...
35 篇 distributed data...
34 篇 fault tolerance
29 篇 concurrent compu...
29 篇 middleware
28 篇 parallel algorit...
28 篇 mapreduce
28 篇 parallel process...
26 篇 resource managem...
25 篇 application soft...
25 篇 big data
24 篇 wireless sensor ...
24 篇 protocols

机构

14 篇 saint petersburg...
7 篇 chinese acad sci...
6 篇 univ adelaide sc...
6 篇 institute of com...
5 篇 zhejiang univ co...
5 篇 shanghai jiao to...
5 篇 plekhanov russia...
4 篇 saint petersburg...
4 篇 harbin inst tech...
4 篇 meshcheryakov la...
4 篇 centre for distr...
4 篇 univ otago dept ...
4 篇 univ stuttgart i...
4 篇 st petersburg st...
4 篇 science and tech...
3 篇 huazhong univ sc...
3 篇 joint institute ...
3 篇 king saud univer...
3 篇 department of co...
3 篇 university of el...

作者

17 篇 zomaya albert y.
11 篇 shen hong
10 篇 bogdanov alexand...
9 篇 korkhov vladimir
8 篇 degtyarev alexan...
8 篇 albert y. zomaya
6 篇 li y
6 篇 taheri javid
5 篇 dustdar schahram
5 篇 leymann frank
5 篇 kopp oliver
5 篇 li kuan-ching
5 篇 kutovskiy nikola...
5 篇 nakayama shigeru
5 篇 huang z.
5 篇 sousa leonel
5 篇 gankevich ivan
5 篇 rothermel kurt
4 篇 jerzy waśniewski
4 篇 leung k.

语言

1,731 篇 英文
18 篇 中文
15 篇 其他
9 篇 俄文

检索条件"任意字段=9th International Conference on Parallel and Distributed Computing, Applications and Technologies"

共 1772 条记录，以下是321-330 订阅

全选清除本页清除全部题录导出标记到"检索档案"

详细简洁

排序：

distributed Trust & Reputation Models using Blockchain technologies for Tourism Crowdsourcing Platforms 10

Distributed Trust & Reputation Models using Blockchain Techn...

引用

10th international conference on Emerging Ubiquitous Systems and Pervasive Networks (EUSPN) / 9th international conference on Current and Future Trends of Information and Communication technologies in Healthcare (ICth)

作者： Veloso, Bruno Leal, Fatima Malheiro, Benedita Moreira, Fernando Univ Porto Campus Fac Engn INESC TEC Rua Dr Roberto Frias Porto Portugal NCI CCC Mayor St Lower Dublin Ireland Polytech Inst Porto Sch Engn IPP ISEP Rua Dr Antonio Bernardino Almeida 431 Porto Portugal Univ Portucalense UPT Rua Dr Antonio Bernardino Almeida Porto Portugal Univ Portucalense REMIT IJP Porto Portugal Univ Aveiro IEETA Aveiro Portugal

Crowdsourced repositories have become an increasingly important source of information for users and businesses in multiple domains. Everyday examples of tourism crowdsourcing platforms focusing on accommodation, food or travelling in general, influence consumer behaviour in modern societies. these repositories, due to their intrinsic openness, can strongly benefit from independent data quality modelling mechanisms. In this context, building trust & reputation models of contributors and storing crowdsourced data using distributed ledger technology allows not only to ascertain the quality of crowdsourced contributions, but also ensures the integrity of the built models. this paper presents a survey on distributed trust & reputation modelling using blockchain technology and, for the specific case of tourism crowdsourcing platforms, discusses the open research problems and identifies future lines of research. 2019 the Authors. Published by Elsevier B.V.

关键词： Trust Reputation Data Integrity Blockchain Social computing.

来源：评论

学校读者我要写书评

暂无评论

DRAGON: A Dynamic Scheduling and Scaling Controller for Managing distributed Deep Learning Jobs in Kubernetes Cluster 9

DRAGON: A Dynamic Scheduling and Scaling Controller for Mana...

引用

9th international conference on Cloud computing and Services Science (CLOSER)

作者： Lin, Chan-Yi Yeh, Ting-An Chou, Jerry Natl Tsing Hua Univ Comp Sci Dept Hsinchu Taiwan

ISBN: (纸本)9789897583650

With the fast growing trend in deep learning driven AI services over the past decade, deep learning, especially the resource-intensive and time-consuming training jobs, have become one of the main workload in today's production clusters. However, due to the complex workload characteristics of deep learning, and the dynamic natural of shared resource environment, managing the resource allocation and execution lifecycle of distributed training jobs in cluster can be challenging. this work aims to address these issues by developing and implementing a scheduling and scaling controller to dynamically manage distributed training jobs on a Kubernetes (K8S) cluster, which is a broadly used platform for managing containerized workloads and services. the objectives of our proposed approach is to enhance K8S with three capabilities: (1) Task dependency aware gang scheduling to avoid idle resources. (2) Locality aware task placement to minimize communication overhead. (3) Load aware job scaling to improve cost efficiency. Our approach is evaluated by real testbed and simulator using a set of TensorFlow jobs. Comparing to the default K8S scheduler, our approach successfully improved resource utilization by 20% similar to 30% and reduced job elapsed time by over 65%.

关键词： Deep Learning Resource Orchestration Deep Learning Job Scheduling Autoscaling

来源：评论

学校读者我要写书评

暂无评论

2019 9th international conference on Computer and Knowledge Engineering, ICCKE 2019

2019 9th International Conference on Computer and Knowledge ...

引用

9th international conference on Computer and Knowledge Engineering, ICCKE 2019

ISBN: (纸本)9781728150758

the proceedings contain 77 papers. the topics discussed include: ELS: an hard real-time scheduler for homogeneous multi-core platforms;MC-RPL: a new routing approach based on multi-criteria RPL for the Internet of things;Persian sentiment lexicon expansion using unsupervised learning methods;a case study for presenting bank recommender systems based on bon card transaction data;performance evaluation of classification data mining algorithms on coronary artery disease dataset;DMap: a distributed blockchain-based framework for online mapping in smart city;and a novel parallel jobs scheduling algorithm in the cloud computing.

关键词：

来源：评论

学校读者我要写书评

暂无评论

the scalability analysis of a parallel tree search algorithm 9th

The scalability analysis of a parallel tree search algorithm

引用

9th international conference on Optimization and applications, OPTIMA 2018

作者： Kolpakov, Roman Posypkin, Mikhail Lomonosov Moscow State University GSP-1 Leninskie Gory Moscow Russia Dorodnicyn Computing Centre FRC CSC RAS Vavilov st. 40 Moscow Russia Moscow Institute of Physics and Technology Institutskiy Pereulok 9 Dolgoprudny Russia Institute for Information Transmission Problems RAS Bolshoy Karetny per. 19 build. 1 Moscow127051 Russia

ISBN: (纸本)9783030109332

Increasing the number of computational cores is a primary way of achieving high performance of contemporary supercomputers. However, developing parallel applications capable to harness the enormous amount of cores is a challenging task. thus, studying the scalability of parallel algorithms (the growth order of the number of processors required to accommodate the growing amount of work, below we give a clear definition of the scalability investigated in our paper) is very important. In this paper we propose a parallel tree search algorithm aimed at distributed parallel computers. For this parallel algorithm, we perform a theoretical analysis of its scalability and show that the achieved scalability is close to the theoretical maximum. © Springer Nature Switzerland AG 2019.

关键词： Scalability

来源：评论

学校读者我要写书评

暂无评论

FPGA-Based parallel Multi-Core GZIP Compressor in HDFS 20

FPGA-Based Parallel Multi-Core GZIP Compressor in HDFS

引用

20th international conference on parallel and distributed computing, applications and technologies, PDCAT 2019

作者： Luo, Haoxin Cai, Ye Luo, Qiuming Mao, Rui College of Computer Science and Software Engineering Shenzhen University Shenzhen China

ISBN: (纸本)9781728126166

With the development of Big Data, data storage has been exposed to more challenges. Data compression which can save both storage and network bandwidth, is a very important technology to deal with the challenges. In this paper, we present an end-to-end, complete, high-throughput parallel multi-core GZIP compressor in FPGA for HDFS. the GZIP compressor is designed by the scalable architecture, which supports to increase throughput by expanding multiple compression cores based on systolic array architecture. We implemented and evaluated the hardware compressor in Alpha Data Adm-Pcie-KU3 FPGA board, utilizing RIFFA for data transfers over PCI Express. According to the evaluation results, up to 16-cores compressor can be implemented and the peak compression throughput exceeds 1.1 GB/s. It is 70X speedup compared with the software compression solution. When we load the hardware compressor into HDFS, the performance of HDFS is twice as much as that without loading the compressor. © 2019 IEEE.

关键词： Field programmable gate arrays (FPGA)

来源：评论

学校读者我要写书评

暂无评论

On the Effects of Data Distribution on Small-error Approximate Adders

On the Effects of Data Distribution on Small-error Approxima...

引用

international conference on Modern Circuits and Systems technologies (MOCAST)

作者： Yizhi Chen Ardalan Najafi Alberto Garcia-Ortiz Universität Bremen Bremen Germany Otto-Hahn-Allee 1 Institute of Electrodynamics and Microelectronics Universität Bremen Bremen Germany

ISBN: (数字)9781728166872

ISBN: (纸本)9781728166889

Approximate computing is a technique to tradeoff accuracy and hardware cost. It increases energy efficiency that leverages application-level tolerance to few errors in many applications including image processing, multimedia, machine learning and wireless communication. Truncated adders, as the most conventional approximate architectures, compute the addition of most significant bits, and produce small errors with high probabilities. In prior art, the adders have been analyzed considering uniformly distributed input data. However, in digital signal processing, the data has a distribution which can be considered as Gaussian distribution characterized by a mean value and standard deviation. this paper studies the effects of input data distribution on small-error approximate adders. We will show that the effects of Gaussian distribution can be modeled for the approximate adder architectures.

关键词： Adders Gaussian distribution Standards Measurement Computer architecture Electronic mail distributed databases

来源：评论

学校读者我要写书评

暂无评论

Quantifying the impact of design strategies for big data cyber security analytics: An empirical investigation 20

Quantifying the impact of design strategies for big data cyb...

引用

20th international conference on parallel and distributed computing, applications and technologies, PDCAT 2019

作者： Ullah, Faheem Babar, M. Ali CREST - Centre for Research on Engineering of Software Technologies University of Adelaide Australia Cyber Security Adelaide University of Adelaide Australia

ISBN: (纸本)9781728126166

Big Data Cyber Security Analytics (BDCA) systems use big data technologies (e.g., Hadoop and Spark) for collecting, storing, and analyzing a large volume of security event data to detect cyber-attacks. the state-of-the-art uses various design strategies (e.g., feature selection and alert ranking) to help BDCA systems to achieve the desired levels of accuracy and response time. However, the use of these strategies in the state-of-the-art is not consistent, which exposes a lack of consensus on 'when to use (and not to use) these design strategies?' In this paper, we follow a systematic experimentation framework to quantify the impact of four design strategies on the accuracy and response time with respect to three contextual factors i.e., security data, machine learning model employed in the system, and the execution mode of the system. For the aimed quantification, we performed experiments on a Hadoop-based BDCA system using four security datasets, five machine learning models, and three execution modes. Our findings lead us to formulate a set of design guidelines that will help researchers and practitioners to decide when to use (and not to use) the design strategies. © 2019 IEEE.

关键词： Cybersecurity

来源：评论

学校读者我要写书评

暂无评论

A holistic stream partitioning algorithm for distributed stream processing systems 20

A holistic stream partitioning algorithm for distributed str...

引用

20th international conference on parallel and distributed computing, applications and technologies, PDCAT 2019

作者： Li, Kejian Liu, Gang Lu, Minhua Guangdong Province Key Laboratory of Popular High Performance Computers Guangdong Province Engineering Center of China-made High Performance Data Computing System College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China School of Biomedical Engineering Shenzhen University Shenzhen518060 China

ISBN: (纸本)9781728126166

the performances of modern distributed stream processing systems are critically affected by the distribution of the load across workers. Skewed data streams in real world are very common and pose a great challenge to these systems, especially for stateful applications. Key splitting, which allows a single key to be routed to multiple workers, is a great idea to achieve good balance of load in the cluster. However, it comes with the cost of increased memory consumption and computation overhead as well as network communication. In this paper, we present a new definition of metric to model the cost of key splitting for intra-operator parallelism in stream processing systems and provide a novel perspective to reduce replication factor while keeping both overall load imbalance and processing latency low. Similar to previous work, our approach treats the head and the tail of the distribution differently in order to reduce memory requirements. For the head, it uses our proposed notion of regional load imbalance to decide dynamically whether to make one more worker responsible for the heavy hitter or not. For the tail, it simply uses hash partitioning to keep the size of the routing table for the head as small as possible. Extensive experimental evaluation demonstrates that our approach provides superior performance compared to the state-of-the-art partitioning algorithms in terms of load imbalance, replication factor and latency over different levels of skewed stream distributions. © 2019 IEEE.

关键词： distributed parameter control systems

来源：评论

学校读者我要写书评

暂无评论

A parallel uncertain frequent itemset mining algorithm with spark 20

A parallel uncertain frequent itemset mining algorithm with ...

引用

20th international conference on parallel and distributed computing, applications and technologies, PDCAT 2019

作者： Ding, Jiaman Li, Haibin Wang, Yuanyuan Jia, Lianyin You, Jinguo Yang, Yang Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming China College of Engineering and Computer Science Australian National University Canberra Australia

ISBN: (纸本)9781728126166

Frequent Itemset Mining (FIM) from large-scale databases has emerged as an important problem in the data mining and knowledge discovery research community. However, FIM suffers from three important limitations with the rapidly expanding of big data in all domains. First, it assumes that all items have the same importance. Second, it ignores the fact that data collected in a real-life environment is often inaccurate. third, it is also a data-intensive and computation-intensive process which makes the FIM algorithm very time-consuming over large datasets. To address these issues, we propose a parallel uncertain frequent itemset mining algorithm with spark (Pufim). Pufim firstly expresses item uncertainty by considering both the probability and weight, and calculates the maximum probability weight value of 1-items. Next, a distributed Pufim-tree structure is designed inspiring by FP-Tree for reducing the times of scanning the databases. Each node of Pufim-tree stores an item and its maximum probability weight value. Finally, experiments on publicly available UCI datasets demonstrate that Pufim achieves more prominent results than other related approaches across various metrics. In addition, the empirical study also shows Pufim has a good scalability. © 2019 IEEE.

关键词： Data mining

来源：评论

学校读者我要写书评

暂无评论

Pipelined Implementation of a parallel Streaming Method for Time Series Correlation Discovery on Sliding Windows 8

Pipelined Implementation of a Parallel Streaming Method for ...

引用

8th international conference on Data Science, Technology and applications (DATA)

作者： Kolev, Boyan Akbarinia, Reza Jimenez-Peris, Ricardo Levchenko, Oleksandra Masseglia, Florent Patino, Marta Valduriez, Patrick INRIA Montpellier France LIRMM Montpellier France LeanXcale Madrid Spain Univ Politecn Madrid UPM Madrid Spain

ISBN: (纸本)9789897583773

this paper addresses the problem of continuously finding highly correlated pairs of time series over the most recent time window. the solution builds upon the ParCorr parallel method for online correlation discovery and is designed to run continuously on top of the UPM-CEP data streaming engine through efficient streaming operators. the implementation takes advantage of the flexible API of the streaming engine that provides low level primitives for developing custom operators. thus, each operator is implemented to process incoming tuples on-the-fly and hence emit resulting tuples as early as possible. this guarantees a real pipelined flow of data that allows for outputting early results, as the experimental evaluation shows.

关键词： Time Series Correlation Data Stream Processing distributed computing

来源：评论

学校读者我要写书评

暂无评论

没有更多数据了...

全选清除本页清除全部题录导出标记到“检索档案”

共178页 << < 29 30 31 32 33 34 35 36 37 38 > >>

检索报告对象比较合并检索0

隐藏清空

合并搜索

回到顶部

执行限定条件

内容：

评分：

请选择保存的检索档案：

请选择收藏分类：

订阅名称：

通借通还

温馨提示：

图书名称：

借书校区：

取书校区：

手机号码：

邮箱地址：

一卡通帐号：

电话和邮箱必须正确填写，我们会与您联系确认。

联系人：

所在院系：

联系邮箱：

联系电话：

内蒙古自治区呼和浩特市赛罕区大学西街235号邮编: 010021

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：