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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191-200 订阅

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the application of blockchain technology in the early warning and monitoring of infectious diseases 5

The application of blockchain technology in the early warnin...

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5th international conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS)

作者： Tian Yu Yu Wan-jun Zhang Miao Zhang Min Tang Jun-yi Shanghai Inst Technol SIT Shanghai Peoples R China

ISBN: (纸本)9781728180229

In order to improve the efficiency of emergency management and decision-making and command of infectious diseases, infectious disease early warning technologies and systems can be used to monitor the trend of abnormal outbreaks and provide timely and accurate warnings. However, the current infectious disease early warning system still has problems such as insufficient intelligence, poor circulation of key information, and difficulty in distributed collaborative decision-making. Based on the blockchain distributed computing architecture, combined with emerging information technologies such as artificial intelligence, big data, and smart contracts, an infectious disease monitoring and early warning technology and its implementation framework are proposed. the technology can efficiently concentrate multi-party monitoring forces, flexibly integrate multiple early warning methods, build a knowledge integration and smart interconnected distributed collaborative monitoring environment under the premise of security and privacy protection, and use smart contracts as open, fair, software-defined monitoring the agent integrates decision-making, monitoring infectious diseases and automatic early warning in real time, so as to take into account the requirements of early warning accuracy and timeliness, and avoid isolated evidence and decision bias.

关键词： Blockchain smart contract artificial intelligence infectious disease monitoring distributed collaboration

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Bloom filter and its variants for the optimization of MapReduce's algorithms: A review 5

Bloom filter and its variants for the optimization of MapRed...

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5th international conference on Cloud computing and Artificial intelligence - Technologies and Applications (CloudTech)

作者： Ezzaki, F. Abghour, N. Elomri, A. Moussaid, K. Rida, M. Univ Hassan 2 Lab Informat Modelisat Syst & Aide Decis LIMSAD Fac Sci Dept Math & Informat Casablanca Morocco

ISBN: (纸本)9781728161754

the bloom filter is a probabilistic data model used to test the existence of an element in a set, i.e., for any given item, the bloom filter could test the membership query on this candidate. the bloom filter has many advantages due to its simplicity and efficiency in highly solving the issue of data representation in many fields and to support membership queries, it has been known as space and time-efficient randomized data structure, by filtering out redundant data and optimizing the memory consumption. However, bloom filters are limited to membership tests and don't support the deletion of elements. they also generate the false positive probability as they are based on a probabilistic model, this error rate is generated when an element that doesn't belong to a set is considered as a member of this set by the bloom filter. Our goal is to compare a number of well- existed algorithms related to the boom filter for future work on the optimization of the join's algorithms in MapReduce. this paper provides an overview of the different variants of the bloom filter and analyses the studies that have been interested in this area of research.

关键词： big data storage optimization bloom filter cuckoo filter membership data structure join algorithms mapreduce

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Prediction Research on Key Influencing Factors of Regional E-commerce Industry Development Based on Grey data Analysis Model 5

Prediction Research on Key Influencing Factors of Regional E...

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5th ieee international conference on Cloud computing and big data Analytics, ICCCBDA 2020

作者： Zhang, Wenyu Wang, Danshu Zhu, Yuting Dong, Dadi Zhao, Songmin Xi'An University of Posts and Telecommunications Xi'an710061 China China Academy of Aerospace Systems Science and Engineering Beijing100048 China

ISBN: (纸本)9781728160245

Based on the gray data analysis model, an improved gray data analysis model for operator is proposed to be applied to the prediction of key factors in the development of regional e-commerce industry. Firstly, the method of fractional order accumulation is improved by the inverse tangent function operator to achieve the effect of improving the accuracy of the operator processing data and making up the operator defect. Secondly, the combination of the improved operator and the gray data analysis model is achieved. the purpose of optimizing the traditional model is as follows. Finally, the application of the key factors affecting the development of e-commerce in Shaanxi Province is analyzed and the feasibility and effectiveness of the model are verified. the results show that the improved model has higher model accuracy and better prediction effect. © 2020 ieee.

关键词： Forecasting

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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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big data integration design for general election in Indonesia 5

Big data integration design for general election in Indonesi...

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5th international conference on Informatics and computing, ICIC 2020

作者： Karya, Gede Sunindyo, Wikan Danar Sitohang, Benhard Akbar, Saiful Mulyanto, Adi School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung Indonesia Department of Informatics Parahyangan Catholic University Bandung Indonesia Bandung Institute of Technology Bandung Indonesia

ISBN: (纸本)9781728180472

the use of big-data analysis in elections in Indonesia has been started since the Governor Election of DKI-Jakarta in 2012 until the Presidential Election in 2019. However, its use is limited to using analytical sentiment to map support and predict election results using social-media data. We see that there is a great opportunity to use big data for a broader election, which is to facilitate the fulfillment of the information and analysis needs of all election stakeholders. But the main problem in using big-data is the integration of big data from various sources with a variety of different formats and large volumes, in addition to the issues of analysis and visualization. For this reason, in this paper, we propose a big-data integration design to meet the needs of elections in Indonesia. this big-data integration design was developed based on election regulations in Indonesia, knowledge of big-data, and the use of a NoSQL database to store unstructured data. the election big-data integration design that we propose includes (1) the information needs of each election stakeholder;(2) the potential for big-data in fulfilling the information needs of every election stakeholder;(3) big-data analysis architecture for elections;(4) big-data integration architecture for elections;(5) crawler architecture;and (5) technology architecture that can implement big-data integration design for elections. Currently, the implementation of this design is in progress in the P3MI-ITB research project. © 2020 ieee.

关键词： big data

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the Influence of All-electronic Interlocking System on Intelligent Operation and Maintenance 5

The Influence of All-electronic Interlocking System on Intel...

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ieee 5th international conference on Intelligent Transportation Engineering (ICITE)

作者： Guan, Weixin Zhou, Xudong UniTTEC Co Ltd Dept Technol Ctr Hangzhou Peoples R China

ISBN: (纸本)9781728194097

With the development of computer industrial control technology, all-electronic interlocking system has become a main development direction of computer interlocking system in our country because of its richer system functions, larger control ranges, smaller volumes, simpler structures, more flexible configurations, and higher electronic levels. the intelligent control of wayside equipment in distributed all-electronic interlocking architecture will greatly improve the function,machine lerning of big data with artificial intelligence, control and maintenance level of wayside basic equipment of railway signaling, so as to fully realize the "revolutionary" improvement of wayside equipment that is still mainly used in the era of electric interlocking system at this stage. this paper summarizes the application scenarios of the all-electronic interlocking system and the industry's main requirements for operation and maintenance, then puts forward four stages and corresponding scenarios of the all-electronic interlocking system on intelligent operation and maintenance. Finally, the future development trend and opportunities of intelligent operation and maintenance in the signaling system are explained.

关键词： Machine Learning Artificial intelligence Intelligent operation and maintenance Electronic interlocking system

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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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LDSQP: Scalable and Efficient Log data Storage and Query Processing Scheme for Cloud data Centers 21

LDSQP: Scalable and Efficient Log Data Storage and Query Pro...

引用

5th international conference on High Performance Compilation, computing and Communications, HP3C 2021

作者： Zhang, Jilian Deng, Xuelian College of Information Science and Technology Jinan University Guangzhou China Faculty of Computer Science Guangxi University of Chinese Medicine China

ISBN: (纸本)9781450389648

Cloud data centers are one of the most important information infrastructures for most real-world applications involving information technology, and serves millions of user requests daily. To provide various services with Quality-of-Service (QoS) guarantees to users, such as low network latency and fast response time, it is vital that cloud data centers should (1) manage its resources efficiently, and (2) avoid resource overprovisioning in situation such as virtual machine allocation. To achieve these goals, cloud data centers usually employ modern techniques from artificial intelligence or machine learning community to analyze status information of cloud data center, so as to find actionable strategies. In this paper, we propose LDSQP, an efficient scheme for storing and querying status information in cloud data centers, i.e., log data that contains enriched information collected from servers, virtual machines, and containers in cloud data centers. We design scalable cluster structure and row key format for log data record in data storage, and we also propose a compact and efficient query expression to capture user's data requirement. We introduce an efficient query processing algorithm that can minimize the number of table partitions to scan. Experiments on a real cluster show that our proposed LDSQP is efficient in log data management. © 2021 ACM.

关键词： Query processing

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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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