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检索条件"任意字段=5th IEEE International Conference on Big Data Intelligence and Computing, DataCom 2019"

共 603 条记录，以下是11-20 订阅

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Using Inaccurate Job Prediction for Detecting Machine Failures in Parallel computing Cluster 5

Using Inaccurate Job Prediction for Detecting Machine Failur...

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5th ieee international conference on big data intelligence and computing, datacom 2019

作者： Ting-Yu, Lin Chou, Jerry National Tsing Hua University Institute of Information Systems and Applications Hsinchu Taiwan National Tsing Hua University Department of Computer Science Hsinchu Taiwan

ISBN: (纸本)9781728141176

Machine failures can cause many problems, such as job execution failures and user data loss. the problem can be further aggravated for parallel computing jobs because a job can be executed across multiple nodes. One of the common approaches is to detect performance degradation as a hint of machine failures before catastrophic impacts occur. However, it is hard to accurately predict job time in practice, especially in a shared and parallel computing environment where performance can be affected by various factors in runtime environment, such as job interference, network delay, etc. therefore, instead of aiming to develop an accurate job predictor, we propose a detection algorithm that incorporates inaccurate job prediction in a more general and complex parallel computing environment. through a series data analysis techniques, we improve the machine failure detection accuracy (i.e., F1-score) by 20% to 40% comparing to the previous approaches. © 2019 ieee.

关键词： data accuracy

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Improve User-Experience of E-Commerce Website by Graph Analysis with Deep Learning 5

Improve User-Experience of E-Commerce Website by Graph Analy...

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5th ieee international conference on big data intelligence and computing, datacom 2019

作者： Wu, Po-Yen Lai, Yu-Rong Chang, Ray-I Liu, Shin-Yat National Taiwan University Department of Engineering Science and Ocean Engineering Taipei Taiwan

ISBN: (纸本)9781728141176

Customers regard good user-experience (UX) as the key factor in online shopping. therefore, e-commerce (EC) websites will track users' behaviors for analyzing their relations to products. According to these analysis results, the websites' layout/workflow and recommendation are tuned to improve UX. However, conventional analysis tools usually focus on user-to-product relations. they are not good at analyzing "big and graphical"user behavior data. In this paper, we develop a graph analysis system with deep learning GNN (graph neural network). Our system uses the standard format xAPI to record user behavior data. We use graph analysis to discover their relations for improving UX. Experiments are on a real EC website with about 6,000 users in 3 months. Our system can reduce over 28% browsing actions of users to reach the page of desired products. © 2019 ieee.

关键词： big data deep learning e-commerce graph analysis graph neural network UX xAPI

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data-Pattern Driven Cache-Collision Timing Attacks Against AES-GCM 5

Data-Pattern Driven Cache-Collision Timing Attacks Against A...

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5th ieee international conference on big data intelligence and computing, datacom 2019

作者： Huang, James Li, Xiaoming University of Delaware Department of Electrical and Computer Engineering NewarkDE19716 United States

ISBN: (纸本)9781728141176

data security is an important issue in big data applications. Even though the same problem has been extensively studied within other contexts, big data applications present several unique challenges. Among them is actually the volume of data that typical big data applications process. Just because there is so much data passing through, the sheer data volume provides way more opportunities for a potential attacker to observe and identify patterns in computation and data. In this paper, we reveal that the data/computation patterns derived from the observation of large volume of data can be associated with certain sensitive information, specifically the key used in the AES-GCM algorithm. the AES-GCM algorithm is one of the foundation algorithms in data security, therefore such data-pattern driven attack can potentially compromise the security of big data application. We present an attack model and an implementation of the attack based on OpenSSL, a widely used library that provides security-related functions for many applications. In most cases, our attack methodology is able to converge and extract the hidden key. We also discuss potential counter measures against similar attacks. © 2019 ieee.

关键词： AES-GCM Cache Collision data Pattern

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A data-Driven Knowledge-Enriched Framework for Dialogue Modeling 5

A Data-Driven Knowledge-Enriched Framework for Dialogue Mode...

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5th ieee international conference on big data intelligence and computing, datacom 2019

作者： Huang, Jhih-Yuan Lee, Wei-Po Tsai, Hsin-Wu Lee, King-Teh National Sun Yat-sen University Department of Information Management Kaohsiung Taiwan Kaohsiung Medical University Department of Healthcare Administration and Medical Informatics Kaohsiung Taiwan

ISBN: (纸本)9781728141176

In recent years, it has been advocated to build social dialogue systems to achieve human-machine interaction. In this study, we adopt deep learning models to develop a dialogue system. In contrast to other studies that mainly concentrate on the model structure and the learning method, our work emphasizes the importance of knowledge and explores the possibility of exploiting knowledge instead. Our system includes a procedure of training a deep learning model to generate answers in response to the users' questions, and most importantly, a set of strategies to exploit various knowledge resources from different perspectives. We have conducted a series of experiments to evaluate the presented approach, and the results confirm its usefulness and effectiveness. © 2019 ieee.

关键词： Contrastive Learning

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A Realtime Crypto computing and Block-Dependability 5

A Realtime Crypto Computing and Block-Dependability

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5th ieee international conference on big data intelligence and computing, datacom 2019

作者： Abhilash, Kancharla Park, Nohpill Oklahoma State University Computer Science StillwaterOK United States

ISBN: (纸本)9781728141176

this paper proposes an analytical approach how to design and realize a crypto computing (Ethereum blockchain-based) under stringent real-time requirement. In order to evaluate the efficacy of the approach, a new analytical metric is defined and developed to estimate the dependability, referred to as the block-dependability. the proposed block-dependability precisely models the probability for the pending transactions to be posted within the current, in other words, within the target block delay, namely, within the deadline required if their expected execution times are within the temporal range of the deadline. Various methods how to prioritize and select transactions in the pending transaction pool in order to facilitate those transactions to be executed within their deadline requirements, such as the normal, random, sorted, and stratified, are proposed and simulated. A set of performance variables, or parameters, such as the number of pending transactions in the pool, the average speed of the transactions, gas fees, deadlines as well as the number of miners, are identified and taken into the block-dependability in order to reveal the influence of each variable on the block-dependability, versus each of those proposed prioritization and selection methods. Extensive parametric simulations are conducted and results are observed and discussed in the cases of the random transaction selection method and the sorted. © 2019 ieee.

关键词： Ethereum

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Offloading Optimization with Delay Distribution in the 3-tier Federated Cloud, Edge, and Fog Systems 5

Offloading Optimization with Delay Distribution in the 3-tie...

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5th ieee international conference on big data intelligence and computing, datacom 2019

作者： Hwang, Ren-Hung Lai, Yuan-Cheng Lin, Ying-Dar National Chung Cheng University Department of Computer Science and Information Engineering Taiwan National Taiwan University of Science and Technology Department of Information Management Taiwan National Chiao Tung University Department of Computer Science Taiwan

ISBN: (纸本)9781728141176

Mobile edge computing and fog computing are promising techniques providing computation service closer to users to achieve lower latency. In this work, we study the optimal offloading strategy in the three-tier federated computation offloading system. We first present queueing models and closed form solutions for computing the service delay distribution and the probability of the delay of a task exceeding a given threshold. We then propose an optimal offloading probability algorithm based on the sub-gradient method. In our numerical results, we show that our simulation results match very well with that of our closed form solutions and our sub-gradient based search algorithm is able to find the optimal offloading probabilities. Specifically, for the given system parameters, our algorithm yields the optimal QoS violating probability of 0.188 with offloading probabilities of 0.675 and 0.37 from fog to edge and from edge to cloud, respectively. © 2019 ieee.

关键词： Mobile edge computing

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Leveraging Reinforcement Learning to Allocate Bandwidth in the Agent-based Resource Management System 5

Leveraging Reinforcement Learning to Allocate Bandwidth in t...

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5th ieee international conference on big data intelligence and computing, datacom 2019

作者： Wang, Mu-Cheng Hershey, Paul C. Ioakimidis, theodore E Raytheon Space and Airborne Systems MarlboroughMA United States Raytheon Intelligence Information and Services DullesVA United States

ISBN: (纸本)9781728141176

In this research, we utilize an agent-based resource management technique, namely the real-time Network Communication Resource Manager (NCRM) to manage battlefield network resources. the NCRM addresses the allocation problem by providing a process, an architecture, and an algorithm to allocate network resources to a mission based on its requirements and currently available resources. this approach enables resilient centralized command (i.e., mission analyses and resource allocation), distributed control, and decentralized execution (i.e., resource reservation and reallocation). Our solution focuses on maximizing the effectiveness of available (remaining) network assets to maintain integrity and efficiency of mission critical assets. NCRM can: (A) through intelligent path selection, deliver mission traffic over the optimal performing paths based on its characteristics or requirements and real-time path status;and (B) effectively converge to a new optimum that dynamically adapts itself to the latest network condition (e.g., newly introduced or lost assets/links). © 2019 ieee.

关键词： Reinforcement learning

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Serverless Skeletons for Elastic Parallel Processing 5

Serverless Skeletons for Elastic Parallel Processing

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5th ieee international conference on big data intelligence and computing, datacom 2019

作者： Kehrer, Stefan Scheffold, Jochen Blochinger, Wolfgang Reutlingen University Parallel and Distributed Computing Group Germany

ISBN: (纸本)9781728141176

Serverless computing is an emerging cloud computing paradigm with the goal of freeing developers from resource management issues. As of today, serverless computing platforms are mainly used to process computations triggered by events or user requests that can be executed independently of each other. these workloads benefit from on-demand and elastic compute resources as well as per-function billing. However, it is still an open research question to which extent parallel applications, which comprise most often complex coordination and communication patterns, can benefit from serverless *** this paper, we introduce serverless skeletons for parallel cloud programming to free developers from both parallelism and resource management issues. In particular, we investigate on the well-known and widely used farm skeleton, which supports the implementation of a wide range of applications. To evaluate our concepts, we present a prototypical development and runtime framework and implement two applications based on our frame-work: Numerical integration and hyperparameter optimization - a commonly applied technique in machine learning. We report on performance measurements for both applications and discuss the usefulness of our approach. © 2019 ieee.

关键词： Cloud platforms

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Automating Literature Reviews: Searching for Behavioral big data in Operations Management 5

Automating Literature Reviews: Searching for Behavioral Big ...

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5th ieee international conference on big data intelligence and computing, datacom 2019

作者： Lee, Hsiao-Hui Mach, Patrizia Shmueli, Galit Yahav, Inbal National Chengchi University Taipei Taiwan National Tsing Hua University Hsinchu Taiwan Tel Aviv University Tel Aviv Israel

ISBN: (纸本)9781728141176

From patient waiting time to consumer shopping, firms collect more and more human behavior data to assist their decision making process. this trend in business also affects academic research, especially in operations management (OM), a research area that often relies on mathematical modeling to guide business decisions. However, it is both time and labor intensive to identify applications and opportunities that use behavioral big data (BBD) in the large and growing published literature. In this paper, we introduce a procedure that applies various data mining approaches to survey a vast number of research articles across three different OM journals, and identify articles that use BBD. the goal is to reduce the number of articles that must be read manually and yet reduce the false negatives (missed BBD papers);in other words, in this classification task we emphasize the importance of sensitivity over specificity with respect to detecting BBD papers. Testing different feature engineering and classification approaches, we find that the highest sensitivity and specificity are provided by a Random Forest classifier, applied to a bag-of-words set of features. © 2019 ieee.

关键词： Information management

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data Analysis of Portfolio Optimization using Artificial Neural Network in China's Stock Market* 5

Data Analysis of Portfolio Optimization using Artificial Neu...

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5th ieee international conference on big data intelligence and computing, datacom 2019

作者： Wang, Chia-Hung Lee, Chia-Jung Fujian University of Technology College of Information Science and Engineering Fuzhou City350118 China Fujian Provincial Key Laboratory of Big Data Mining and Applications No. 33 South University Road University Town Fujian Province Fuzhou City350118 China Soochow University School of Big Data Management No. 70 Linhsi Road Shihlin District Taipei City11102 Taiwan

ISBN: (纸本)9781728141176

this paper presents a neural network model for studying the portfolio optimization in China's stock market. An investment scheme is proposed by using an artificial intelligence algorithm based on the trained neural network. In the empirical study, six key indicators of historical transaction data for stocks are used as the input characteristics of our neural network model, which are the opening price, the closing price, the trading volume, the highest and lowest price on the day, and Week time scale. Besides, we use Python technology to process the collected stock data and develop the learning process of the studied neural network for predicting the closing price for the next period. Based on the presented data analysis in the paper, the investors of the stock market in China could make a rational decision-making on the portfolio optimization. © 2019 ieee.

关键词： Neural network models

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