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SC 2017 - international conference for High Performance computing, networking, Storage and Analysis

SC 2017 - International Conference for High Performance Comp...

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2017 international conference for High Performance computing, networking, Storage and Analysis, SC 2017

ISBN: (纸本)9781450351140

The proceedings contain 64 papers. The topics discussed include: Egeria: a framework for automatic synthesis of HPC advising tools through multi-layered natural language processing;optimizing the query performance of block index through data analysis and I/O modeling;Sympiler: transforming sparse matrix codes by decoupling symbolic analysis;control replication: compiling implicit parallelism to efficient SPMD with logical regions;efficient process mapping in geo-distributed cloud data centers;topology-aware GPU scheduling for learning workloads in cloud environments;probabilistic guarantees of execution duration for amazon spot instances;a framework for scalable biophysics-based image analysis;and exploring and analyzing the real impact of modern on-package memory on HPC scientific kernels.

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UR Rank: Micro-blog User Influence Ranking Algorithm Based on User Relationship 13th

UR Rank: Micro-blog User Influence Ranking Algorithm Based o...

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13th European-Alliance-for-Innovation (EAI) international conference on Collaborative computing - networking, Applications and Worksharing (CollaborateCom)

作者： Yao, Wenbin Yang, Yiwei Wang, Dongbin Beijing Univ Posts & Telecommun Beijing Key Lab Intelligent Telecommun Software & Beijing Peoples R China Beijing Univ Posts & Telecommun Natl Engn Lab Mobile Network Secur Beijing Peoples R China Beijing Univ Posts & Telecommun Minist Educ Trustworthy Distributed Comp & Serv Key Lab Beijing Peoples R China

ISBN: (纸本)9783030009168;9783030009151

In this paper, a novel UR Rank (User Relationships based Ranking) algorithm is proposed for ranking the influence of the user. We first explore five factors that affect user relationship. They are following rate (FR) factor, activity (ACT) factor, authority (ATR) factor, interaction (ITA) factor and similarity (SML) factor. Then those factors are used in Support Vector Regression (SVR) model to predict the relationship between users. We assimilate such predicted relationship into a PageRank based transition probability to identify influential users. The experiments on a real micro-blog data set demonstrate that UR Rank algorithm has better performance and is more persuasive than the existing algorithms.

关键词： Micro-blog User influence Support vector regression PageRank

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Real-Time Traffic Congestion Analysis Based on Collected Tweets

Real-Time Traffic Congestion Analysis Based on Collected Twe...

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IEEE international Symposium on Parallel and distributed Processing with Applications and IEEE international conference on Ubiquitous computing and Communications (ISPA/IUCC)

作者： Mohammed A. A. Al-qaness Mohamed Abd Elaziz Ammar Hawbani Aaqif Afzaal Abbasi Liang Zhao Sunghwan Kim Wuhan University Wuhan China Department of Mathematics Zagazig University Zagazig Eygept School of Computer Science and Technology University of Science and Technology of China Anhui China Department of Software Engineering Foundation University Islamabad Pakistan School of Computer Science and Technology Shenyang Aerospace University Shenyang China School of Electrical Engineering University of Ulsan Ulsan Korea

ISBN: (纸本)9781728152103

Due to the rapid increase in the use of and advancement of social media platforms, the amount of data available on the internet is increasing. The available information on the internet can be used to gain insights about searching trends and public interests. The advancements in machine learning and deep learning techniques drastically improved data analytics and processing solutions for social media and infotainment industry. It is no doubt that the majority of regular commuter encounters traffic congestion daily. There is a growing number of population in Metropolitans. This leads to higher population density rates and traditional methods for collecting traffic information using physical sensors are expensive, however, by using social media tools information regarding traffic jam, road and traffic congestion can be improved. In this paper, we analyze traffic congestion using Twitter data (tweets) in real-time. The proposed model extracts traffic-related tweets from Twitter and classifies the extracted information for traffic commute estimation road. In this study, tweets from Los Angeles, USA are taken into consideration as an analysis example. A machine learning classifier and a deep learning classier are used to classify traffic information. The model was trained to collect tweets containing the word 'traffic' in a real-time environment.

关键词： data analysis learning (artificial intelligence) social networking (online) traffic information systems Traffic information systems traffic congestion Social Media Data analysis Traffic information TWITTER Internets physical sensor SOLUTION PROCESSING real-time traffic Statistical analysis

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14th international conference on Emerging networking EXperiments and Technologies (CoNEXT)

作者： Li, Hangyu Xu, Hong Nutanong, Sarana City Univ Hong Kong Hong Kong Peoples R China Vidyasirimedhi Inst Sci & Technol Wangchan Thailand

ISBN: (纸本)9781450360807

We propose Bohr, a similarity aware geo-distributed data analytics system that minimizes query completion time. The key idea is to exploit similarity between data in different data centers (DCs), and transfer similar data from the bottleneck DC to other sites with more WAN bandwidth. Though these sites have more input data to process, these data are more similar and can be more effciently aggregated by the combiner to reduce the intermediate data that needs to be shuffled across the WAN. Thus our similarity aware approach reduces the shuffle time and in turn the query completion time (QCT). We design Bohr based on OLAP data cubes to perform efficient similarity checking among datasets in different sites. We implement Bohr on Spark and deploy it across ten sites of AWS EC2. Our extensive evaluation using realistic query workloads shows that Bohr improves the QCT by up to 50% and reduces the intermediate data by up to 6x compared to state-of-the-art solutions that also use OLAP cubes.

关键词： Data Analytics Cloud computing WAN

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Dynamic Network Embedding for Link Prediction

Dynamic Network Embedding for Link Prediction

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IEEE international conference on Big Data and Cloud computing (BdCloud)

作者： Yan Cao Yihong Dong Shaoqing Wu Yu Xin Jiangbo Qian Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo China

ISBN: (数字)9781728143286

ISBN: (纸本)9781728143293

Link prediction aims to predict the occurrence of link between two nodes. Most existing efforts focus on the static networks, which preserve the network information without temporal information. However, real-world networks dynamically evolve over time. In this paper, we propose a dynamic network link prediction probability model. Specifically, the model captures the local structural features by preserving the similarity between nodes. It also uses dynamic logistic regression model to learn the dynamic evolution mode of the network from the second-order similarity. Temporal information and structural features of the network are obtained from network snapshots. In the proposed method, the objective function combines both local and dynamic evolution structure, that minimize the proximity of similar nodes in embedded space. The stochastic gradient descent method is adopted to solve the optimization task. Experiments show that our model has significantly better link prediction performance on many real dynamic networks than existing methods.

关键词： C# languages distributed processing Big Data Cloud computing Social computing

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SAND: A Social-Aware and distributed Scheme for Device Discovery in the Internet of Things

SAND: A Social-Aware and Distributed Scheme for Device Disco...

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2018 international conference on computing, networking and Communications, ICNC 2018

作者： Sunthonlap, James Nguyen, Phuoc Wang, Hao Pourhomanyoun, Mohammad Zhu, Yuqing Ye, Zilong California State University Los Angeles 5151 State University Drive Los AngelesCA90032 United States Clean Energy Institute University of Washington SeattleWA98195 United States

ISBN: (纸本)9781538636527

The Internet of Things (IoT) is continuously growing to connect billions of smart devices anywhere and anytime in an Internet-like structure, which enables a set of new applications, services and interactions between human and objects. In the future, the smart devices are supposed to be able to autonomously discover a target device with desired features and cooperate on generating new services. The pervasiveness of smart devices, as well as the heterogeneity of their design and functionalities, raise a major concern: How can a smart device efficiently discover a desired target device? In this paper, we propose a Social-Aware and distributed (SAND) scheme that achieves a fast, scalable and effective device discovery in the IoT. The proposed SAND scheme adopts a novel device ranking criteria that measures the device's degree, social relationship diversity, clustering coefficient and betweenness. Based on this, the discovery request can be guided to travel through critical devices that stand at the major intersections of the network, and thus quickly reach the desired target device while contacting only a limited number of intermediate devices. We conduct comprehensive simulations to evaluate SAND in terms of the discovery success rate and the number of devices contacted. The simulation results demonstrate the effectiveness of SAND. © 2018 IEEE.

关键词： Internet of things

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Mining Health Discussions on Suomi24

Mining Health Discussions on Suomi24

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IEEE international conference on Big Data and Cloud computing (BdCloud)

作者： Moamen Ibrahim Matti Eteläperä Sercan Turkmen Mina Maged Mourad Oussalah Jouko Miettunen Centre for Machine Vision Research University of Oulu Oulu Finland Pepron Software Services Ltd. Oulu Finland Lifelong Health Research Unit University of Oulu Oulu Finland

ISBN: (数字)9781728143286

ISBN: (纸本)9781728143293

This paper discusses an effective way to fuse multiple tools to make a modern and easy-to-use software to get some key findings about health related topics on online social platforms, with a special focus on the famous Finnish forum, Suomi24. Several natural language processing have been tested and, sometimes, modified in order to accommodate the Finnish language linguistic structure and achieve our tasks of mining the health discussion content. We also explore the ability to monitor and track diseases in Finish open discussion forum. The developed modular system can help clinicians and medical experts to analyze similar forums to identify and track related events that can be correlated with hospital dataset in order to generate new hypotheses that initiate future treatment based approaches.

关键词： Tools Diseases Task analysis Social networking (online) Data mining Machine vision Natural language processing

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A Service-based Fog Execution Environment for the IoT-aware Business Process Applications 25

A Service-based Fog Execution Environment for the IoT-aware ...

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25th IEEE international conference on Web Services (IEEE ICWS) Part of the IEEE World Congress on Services

作者： Cheng, Yongyang Zhao, Shuai Cheng, Bo Chen, Junliang Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing Peoples R China

ISBN: (纸本)9781538672471

With the fast development of Internet of Things (IoT), a large amount of services are being generated continuously by different business process applications hosted on edge devices. In order to facilitate seamless access and service life cycle management of large, distributed and heterogeneous IoT services, service computing and fog computing have been widely used as the promising technologies. However, an execution environment integrating IoT services into these two technologies is still an open research challenge. In this paper, we proposed a novel service-based fog execution environment to make the business process applications fit in the dynamic IoT service environment. The proposed IoT execution environment promises a full-life cycle management of the IoT services, a low latency response of the edge devices and a distributed execution of the business process applications. An actual running intelligent medical case is given to validate our proposed IoT execution environment.

关键词： Internet of Things service management fog computing business process application wireless sensor networks

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An Edge-Assisted Vehicle Routing Method Based on Game-Theoretic Multiagent Learning

An Edge-Assisted Vehicle Routing Method Based on Game-Theore...

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international conference on Parallel and distributed Systems (ICPADS)

作者： Bichuan Zhu Jinglin Li Quan Yuan Jiawei Lu Shu Yang State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China Datang Network CO. LTD. Beijing China

ISBN: (纸本)9781728125848

Traffic congestion is a serious social issue confronting modern cities. To improve traffic efficiency, route planning for the individual vehicle based on dynamic traffic conditions has been well investigated. However, existing studies usually model the vehicles with perfect rationality and omit the complex routing interaction among multiple vehicles, causing that the routing performance dramatically deviates from the system optimum. Considering the mutual influence in multi-vehicle routing, we propose an edge-assisted distributed routing framework, which enables virtual agents on behalf of vehicles to interact with others to make real-time routing decisions. In the framework, we model the agent interaction as a population game and propose a scalable multiagent learning algorithm to efficiently find the Nash equilibria. Specifically, the multiagent learning algorithm takes the bounded rationality of individuals into account and uses a stage learning algorithm based on best-response dynamics to plan a route for vehicles. The simulation results show that the algorithm efficiently converges to Nash equilibria, and achieves the maximum throughput at the intersection and the minimum average travel time for vehicles.

关键词： Vehicle routing Edge computing Population game Multiagent learning Vehicle routing vehicle system optimization Network routing learning algorithms traffic congestion Nash equilibrium Reasoning rationality

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distributed Parallelizability Analysis and Optimization of Legacy Code in Cloud Migration

Distributed Parallelizability Analysis and Optimization of L...

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IEEE international conference on Big Data and Cloud computing (BdCloud)

作者： Junfeng Zhao Ziyang Qin Hongji Yang College of Computer Science Inner Mongolia University Hohhot China Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Hohhot China Department of Informatics University of Leicester Leicester England

ISBN: (数字)9781728143286

ISBN: (纸本)9781728143293

More and more organizations plan to migrate their legacy systems to cloud so as to improve the efficiency of data processing. In order to take full advantage of the parallel virtue of cloud computing, legacy system need to be refactored according to the cloud computing program model. Before that, the parallelizability analysis is the first thing to do. This paper first proposes distributed parallelizability of legacy code in the process of refactoring from Legacy code to Mapreduce code (DPLM), and then derives four parallel-determinable features according to the rule: data dependency, continuous dependency, non-homology and randomness. Then algorithms are developed to detect parallel-determinable features. Only the legacy code which is not satisfied with all the four-point features can be refactored to parallelizable MapReduce code. However, through practice, the situation is discovered that there are some type of legacy codes can't take advantage of the parallelism of MapReduce. To solve this problem, parallel-determinable features is divided into strong and weak features. Weak features can be resolved by reorganizing source input file. Partial strong features can be resolved by iterative grading. Finally, the experiments show the validity of DPLM and optimization method, parallel-determinable features and optimization methods.

关键词： cloud computing data handling parallel processing software maintenance data processing Cloud computing Software maintenance PARALLEL PROCESSING (COMPUTERS) Legacy Legacy systems Codes Clouds

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