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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151-160 订阅

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the Digital Construction in Multinational Corporations: A Preliminary Study

The Digital Construction in Multinational Corporations: A Pr...

引用

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

作者： Zhengkai Zhang Shengzhi Xu State Grid International Development Co. Ltd. Beijing China

ISBN: (纸本)9781665420723

the international market environment is undergoing rapid change. To advance the development of multinational corporations, the information systems in multinational corporations are expected to be constructed rapidly and optimized timely in accordance with the requirements of international business management. Moreover, the cross-border data in multinational corporations is massive, from which valuable insights can be discovered to support international business management. In this paper, we analyze the challenges of constructing information systems in multinational corporations. We propose that the new architecture with the enterprise middle platform as core should be used in multinational corporations. this architecture could accumulate common system functions, collect business data resources, and provide convenient and reusable services to the front-end information systems. Based on the enterprise middle platform, we could accelerate the construction of the information systems and conduct various data analysis. An information system is developed as an example to show that adopting this new architecture is good for multinational corporations to implement global management. the study in this paper provides meaningful references for multinational corporations to drive digital transformation.

关键词： data analysis Digital transformation conferences Computer architecture big data Information systems Business

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big data intelligence Solution for Health Analytics of COVID-19 data with Spatial Hierarchy

Big Data Intelligence Solution for Health Analytics of COVID...

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

作者： Carson K. Leung Chenru Zhao University of Manitoba Winnipeg MB Canada

ISBN: (纸本)9781665420723

In the current era of big data, technological advancements have made it easy and quick to generate and collect huge volumes of varieties of data from wide ranges of rich data sources. these big data may be of different levels of veracity, including precise data and imprecise or uncertain data. Embedded in the data are valuable information and useful knowledge that can be discovered by big data intelligence and computing. In this paper, we propose a big data intelligence solution for health analytics with spatial hierarchy. In particular, we focus on analyzing coronavirus disease 2019 (COVID-19) epidemiological data at different spatial granularity levels. Since its outbreak, there have been cumulatively millions of COVID-19 cases observed in various spatial locations around the world. Our solution focuses on analyzing and mining valuable information and useful knowledge (e.g., distribution, frequency, patterns) of health-related states and characteristics in populations in various spatial locations in a top-down fashion along the spatial hierarchy. To reduce redundancy, our solution discovers and returns to users (e.g., researcher, civilian) new information and knowledge not found at previous spatial hierarchical levels. the discovered information and knowledge helps the users to understand the disease better, and thus take an active role to fight, control, and/or combat the disease. Evaluation of our big data intelligence solution on real-life COVID-19 data demonstrates its practicality in health analytics of the data with spatial hierarchy and in revealing new knowledge about COVID-19 cases at different spatial granularity levels. the solution is expected to be adaptable to health analytics of other diseases.

关键词： COVID-19 Soft sensors conferences Sociology Redundancy big data data science

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Elastic Service Provisioning for Mobile Edge computing

Elastic Service Provisioning for Mobile Edge Computing

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

作者： Xuefei Duan Hua Lu Sisi Li GuangDong Communications and Networks Institute Guangzhou China Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9781665420723

Major mobile edge computing service providers employ container-based virtualization so as to implement elastic service offerings. Prior works mainly dedicate to service deployment toward lower service latency and better edge resource utility. Due to the time-varying workloads at the edge, it is of critical importance to consider elastic service provisioning. In elastic service provisioning, container images need to be frequently fetched from remote cloud to edge servers, which may incur significant overhead in terms of latency and network resource consumption. Given the limited resources at the edge, we need to minimize such overhead. We exploit service migration in elastic service provisioning to reduce the overhead in terms of latency and core network resource consumption. Taking into account the dynamic of the workloads and the locations of current deployed service entities, we investigate to study the elastic service provisioning problem in this paper. We formulate the problem into an optimization problem and a greedy algorithm is designed to solve the problem. Extensive experiments validate the efficiency of our algorithm.

关键词： Greedy algorithms Multi-access edge computing Image edge detection Heuristic algorithms conferences Containers Servers

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A Feature and Deep Learning Model Recommendation System for Mobile Application

A Feature and Deep Learning Model Recommendation System for ...

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

作者： Xiangying Che Qibo Sun Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9781665420723

Deep learning is becoming more and more popular in mobile applications. We also find apps using deep learning are more popular. For app designers and developers, it is particularly important to filter out certain features of corresponding scenarios and target user groups effectively from complex and diverse deep learning techniques, which is hard for developers. To solve this problem, we design a mobile deep learning application features and models recommendation system for app designers in this paper. Designers only need to input the description information of their app to acquire some appropriate features and models advice. Firstly, we analyze the feasibility and necessity of mobile application feature and model recommendation. then the database of app details is built and sorted out to provide the basis for the recommendation system. Finally, we give out a new recommendation system specially designed for mobile apps on Google App Store, which mainly includes Labeled-LDA text classification and BERT text similarity matching combined with recommendation correction. the demonstration shows that our system has some reference value for mobile application developers.

关键词： Deep learning databases conferences Computational modeling Bit error rate Text categorization big data

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Research on data Acquisition of Intelligent Network Industry Chain Based on data Integration and Self-Acquisition Technology

Research on Data Acquisition of Intelligent Network Industry...

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ieee international conference on Artificial intelligence and Computer Applications (ICAICA)

作者： Lei Liu Qianlong Feng Song Wu Fan Zhang China Automotive Technology and Research Center Co. Ltd. Tianjin China China Auto Information Technology (Tianjin) Co. Ltd. Tianjin China

the process modeling method of supply chain is established. this project intends to adopt “access to associated information-assimilation and integration of multi-source information-large-scale information analysis and processing - multi-agent interactive application” as the main line to study the industrial collaborative support method based on industrial chain collaborative support services. the calculation methods of supply and demand chain, enterprise chain, value chain and space chain are presented. By collecting the basic information of the enterprise, itself, the link between the supply chain and the enterprise is found. the main research contents include large-scale data mining and analysis processing; data analysis based on domain ontology; Information coding specification. the system uses *** MVC system architecture, and carries on the structured database design with SQLServer2012. An integrated application simulation experiment platform is constructed with distributed cloud computing architecture. this paper discusses the development and use of intelligent monitoring and big data collection system of supply chain from the aspects of enterprise management process and human power. through the experiment simulation, the matching algorithm and the corresponding optimization method of the industry cooperation support can lay the foundation for the reasonable and reasonable development of enterprises.

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Optimization of Text Generation Method for Task-based Human-machine Dialogue System

Optimization of Text Generation Method for Task-based Human-...

引用

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

作者： Zixuan Guo Qibo Sun State Key Laboratory of Networking and Switching Technology (BUPT) Beijing China

ISBN: (纸本)9781665420723

the human-machine dialogue system is now used in all aspects of life, but the task-based system reply relies on manually marked rules and templates, which makes the text rigid and monotonous, and can not form coherent paragraphs when there is more information, but simply combine sentences together. In order to solve the above problems, this article first analyzes the construction method of the task-based dialogue system, and then proposes an optimized text generation method for the pipeline method. this paper uses the Planning-based Hierarchical Variational Model (PHVM) in the text generation phase. this model will make a global plan before generating the text, split the long text generation task into clauses, and retain the relevance between the clauses, and then generate clauses one by one. In addition, it uses sampling to introduce latent variables globally and locally to provide a variety of options for text generation. Experiments show that the model performs well in generating coherent text and rich text diversity, and solves the existing problems of task-based dialogue systems.

关键词： conferences Computational modeling Pipelines big data Task analysis Man-machine systems Optimization

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Supervised learning and unsupervised learning on music data with different genres

Supervised learning and unsupervised learning on music data ...

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

作者： Chunang Liu Zehan Chao Jining China Beijing China

ISBN: (纸本)9781665420723

As the digital music datasets evolve fast in the past decades, researchers start to focus on the machine learning tasks on music datasets with music content analysis. Both supervised learning and unsupervised learning have become increasingly popular as searching and recommending are the major tasks for digital music datasets. In this work, we are interested in exploring automatic classification of music with different genres. In this paper, we visualize and embed Spotify music in 3 dimension space with principal component analysis techniques; classifying different music genres via MFCC related algorithms and various machine learning algorithms. We use the method of PCA to determine the relationship between each feature of those songs in the data including 154932 songs and utilize different visualization techniques to obtain insight into the dataset. thus, we could get the similarity of the songs by analyzing the two clusters of points on the graph. Additionally, we use numerous supervised learning techniques to perform classification on those songs in ten genres and evaluating their performance. Experimental results show the feasibility of automatic management of music databases and the potential to improve.

关键词： Machine learning algorithms databases conferences Supervised learning data visualization Machine learning Task analysis

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the Platform Construction of the Traffic Management Smart Police Center under the Background of "Internet plus " 5

The Platform Construction of the Traffic Management Smart Po...

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5th international conference on IoT in Social, Mobile, Analytics and Cloud (I-SMAC)

作者： Mu, Xiaoyang Railway Police Coll Zhengzhou 450053 Henan Peoples R China

ISBN: (纸本)9781665426428

In accordance with the overall development strategy deployment of national transportation construction, through systematic research on the in-depth application of big data, cloud computing, artificial intelligence and Internet of things technology in road traffic management, sdentifically formulate the main content and framework system for the development of smart transportation under the Internet + background, and comprehensively Grasping the trends and characteristics of the future development of China's smart transportation management has important academic value and research significance for improving the level of my country's smart transportation management.

关键词： Traffic Management Smart Police Internet big data

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Massive AI based cloud environment for smart online education with data mining 5

Massive AI based cloud environment for smart online educatio...

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5th international conference on Intelligent computing and Control Systems, ICICCS 2021

作者： Pei, Ying Li, Gang Xi'An Siyuan University Xi'an Shaanxi710038 China Xi'An Thermal Power Research Institute Co. Ltd Xi'an Shaanxi710043 China

ISBN: (纸本)9781665412728

Under the background of the deep integration of Internet technology and artificial intelligence technology with the field of education, the traditional teacher centered teaching mode is facing a historic change. How to guide students to learn and communicate actively, and explore and improve the new student-centered teaching mode, has become the key problem to be solved. In the mixed cloud environment, the data stream is disturbed, and the error of mining association data is large. Aiming at the problem of poor anti-interference of scattered point cloud adaptive compression mining algorithm, this paper analyzes the data mining technology in cloud environment. Firstly, the time series analysis model of big data information flow in hybrid cloud environment is constructed to analyze the data structure, and then the high-dimensional phase space of data information flow in hybrid cloud environment is reconstructed. In the reconstructed phase space, the association rules are extracted, and the extracted features are used as pheromones to guide data location mining, so as to improve the data mining algorithm. © 2021 ieee.

关键词： data mining

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Research on the impact of digital finance on greener emissions based on the fixed effect model with panel data 22

Research on the impact of digital finance on greener emissio...

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Proceedings of the 2022 5th international conference on E-Business, Information Management and Computer Science

作者： Guiping Chen Wuhan Business University China and Research Center for Technology and Finance of Wuhan Business University China

ISBN: (纸本)9781450397827

With the rapid development of big data, artificial intelligence and cloud computing, digital finance is becoming more and more popular. Based on the fixed effect model and using the panel data of 260 cities from 2011 to 2019, this paper focuses on the relationship between the development of digital finance and carbon emission reduction. the study found that the development of digital finance has significantly promoted carbon emission reduction, and the coverage and depth of digital finance also have a significant role in promoting carbon emission reduction. At the same time, this paper finds that the development of digital finance can help reduce carbon emissions by improving the efficiency of pollution control.

关键词： artificial intelligence big data Digital finance carbon emission reduction pollution control

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