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检索条件"任意字段=2019 International Conference on Modeling, Simulation and Big Data Analysis（MSBDA 2019）"

共 1293 条记录，以下是1-10 订阅

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Supply Chain Network Optimization in Fruit Industry Based on big data analysis 24

Supply Chain Network Optimization in Fruit Industry Based on...

引用

5th international conference on Artificial Intelligence and Computer Engineering, ICAICE 2024

作者： Liu, Dalong Mo, Quanli Nanning University Guangxi Nanning China

ISBN: (纸本)9798400718007

With the rapid development of big data technology, its application in supply chain management is becoming increasingly widespread. In traditional supply chain management, the acquisition and analysis of relevant data are often based on limited information and experience, which makes supply chain decisions easily influenced by individual subjective factors and lacking in scientific basis. The application of big data breaks through this limitation by collecting and analyzing data from various links in the supply chain, enabling enterprises to have a comprehensive understanding of the operation of the supply chain and make more accurate decisions. This paper takes Nanning Haijixing Fruit wholesale market as the research object, uses SPSS software for big data analysis, and aims to help enterprises optimize supply chain network and reduce transportation costs through in-depth mining and analysis of multi-dimensional data such as market demand and transportation cost. The results show that big data analysis can significantly improve supply chain efficiency, reduce transportation costs, and provide strong support for the sustainable development of Nanning agricultural products market. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： big data analysis

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Prediction of commodity demand in the supply chain of e-commerce platform using big data analysis - taking apparel e-commerce company as an example 24

Prediction of commodity demand in the supply chain of e-comm...

引用

5th international conference on Artificial Intelligence and Computer Engineering, ICAICE 2024

作者： Liu, Dalong Nanning University Guangxi Nanning China

ISBN: (纸本)9798400718007

This paper takes the apparel e-commerce company as the research object, discusses the method and application of using big data analysis to forecast the commodity demand in its supply chain. As an important enterprise in the field of fast fashion e-commerce, clothing e-commerce companies are faced with complex and changing market demand. Through the effective management and analysis of its big data, the establishment of commodity demand forecasting model is helpful to optimize the supply chain decision, improve the efficiency of inventory management, meet the needs of consumers, and enhance the competitiveness of enterprises. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Marketplaces

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analysis and prediction of online learning behavior based on data mining technology 3

Analysis and prediction of online learning behavior based on...

引用

3rd international conference on Electronic Information Engineering, big data, and Computer Technology, EIBDCT 2024

作者： Ye, Chengqiong Chen, Yang School of Big Data and Artificial Intelligence Anhui Xinhua University Hefei230088 China

ISBN: (纸本)9781510680449

With the development of big data and education informatization, the learning platform is generating a large amount of data. By mining and analyzing these behavioral data, we can have a better understanding of students' online learning situation, which is helpful to carry out real-time intervention and targeted guidance for students in the learning process, so as to achieve the goal of personalized training. Therefore, this paper uses the learning behavior data generated by students' online learning platform for cluster analysis, and then uses correlation analysis, regression analysis, CHAID algorithm, Logistic regression modeling algorithm, etc., to predict different students' online learning behavior models, which has certain practical significance and application value. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Students

来源：评论

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Research on shipping statistics method based on AIS big data mining analysis 4

Research on shipping statistics method based on AIS big data...

引用

4th international conference on Geology, Mapping, and Remote Sensing, ICGMRS 2023

作者： Zhao, Longfei Jiang, Xiaoyi Lu, Wenhu Kang, Linchong Wang, Yi Technology Innovation Center of Marine Information MNR Tianjin China National Marine Data and Information Service Tianjin China Key Laboratory of Urban Land Resources Monitoring and Simulation Ministry of Natural Resources Shenzhen China

ISBN: (纸本)9781510672741

In order to solve the problems of traditional shipping statistics, this paper puts forward the method of shipping statistics based on AIS big data, and gives complete technical process and technical scheme including big data platform construction, data access, data cleaning, data warehouse construction, navigation event analysis, voyage number mining and generation of statistical index and systematically applies the above processes and schemes. Firstly, based on the distributed storage and computing framework, the four-layer system architecture of data acquisition and processing layer, data storage layer, data analysis layer and service encapsulation layer is used to put forward the construction technology method of AIS big data platform, which meets the requirements of real-time AIS stream data acquisition, historical AIS file extraction, data cleaning, data storage management, data mining analysis, statistical analysis, visual display and application service;Secondly, AIS big data mining analysis model including electronic fence analysis, berth supplement, berth commodity identification, navigation event analysis, voyage number mining, voyage cargo capacity calculation and other model algorithms is constructed and integrated into the big data platform to meet the needs of AIS big data mining analysis and statistical analysis;Finally, taking the application of AIS data of global bulk cargo ships from March to May of 2019 as an example, it shows that this method can provide statistical indicators of shipping big data in three aspects including port, sea passage and bulk cargo, and provides a new idea for realizing real-time, accurate and refined statistical analysis and display of shipping. © 2024 COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： big data

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big data Optimization Clustering Algorithm for Power Grid CPS Based on PSO 2

Big Data Optimization Clustering Algorithm for Power Grid CP...

引用

2nd international conference on data Science and Information System, ICDSIS 2024

作者： Yue, Junying Arimuzha Inner Mongolia Normal University College Of Computer Science And Technology Hohhot China

ISBN: (纸本)9798350381665

As power systems become increasingly intelligent and internet of things technology finds widespread use, the power grid cyber-physical system (CPS) has become pivotal in real-time monitoring, data processing, and intelligent control. Nonetheless, the generation of large-scale, high-dimensional real-time data within the power grid CPS system poses challenges to traditional data processing and analysis methods. In order to meet this challenge, this paper proposes a PSO-based power grid CPS big data optimization clustering algorithm. By introducing PSO algorithm, the algorithm makes full use of its global search and adaptive characteristics to cluster the massive data in the CPS system of power grid efficiently. Compared with the traditional clustering algorithm, the algorithm proposed in this paper is superior in mining data relations and finding abnormal situations. In the experimental verification stage, we use different scale and complexity power grid data sets to verify the feasibility and performance of the algorithm. The experimental results show that the power grid CPS big data optimization clustering algorithm based on PSO has achieved significant performance improvement in multiple data scenarios. The research results of this paper have important theoretical and practical significance for intelligent management, data mining and anomaly detection of power grid system. © 2024 IEEE.

关键词： Clustering algorithms

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Research on Computer big data Industrial Structure Adaptation simulation System 6

Research on Computer Big Data Industrial Structure Adaptatio...

引用

2023 IEEE 6th international conference on Information Systems and Computer Aided Education, ICISCAE 2023

作者： Chao, Wang Weike, Lai Xuxi, Yin Zhe, Wang Guangzhou Huashang Vocational College Guangdong Guangzhou511300 China

ISBN: (纸本)9798350313444

This system is an educational big data industrial structure adaptation simulation system integrating big data technology, information technology, data visualization technology and automation technology. It is a computer science decision support system aiming at automatic collection, intelligent storage, scientific decision making and intelligent application. A two-level resource scheduling mechanism is constructed to decouple the extended heterogeneous application of teaching resource scheduling and teaching resource allocation. Encapsulate applications and isolate running environments through lightweight container technology to achieve dynamic application expansion and deployment management. It is found that the data collection and analysis of teaching resources of the whole big data industry structure ADAPTS simulation system based on open protocol. © 2023 IEEE.

关键词： adaptation simulation big data big data deep learning industrial structure node centrality

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Fusion modeling and Heterogeneous simulation of big data Technology in 5G Communication Network 4

Fusion Modeling and Heterogeneous Simulation of Big Data Tec...

引用

4th international conference on Smart Systems and Inventive Technology, ICSSIT 2022

作者： Liu, Taiwei Wang, Caifeng Fu, Haiyan Lei, Tengfei Qilu Institute of Technology Shandong Jinan250000 China

ISBN: (纸本)9781665401180

In order to continuously improve the service capabilities of the 5G communication network and promote the formation of a high-quality and efficient service structure, the R&D team and technical personnel have gradually tried to apply big data technology to the 5G communication network and achieved certain results. In this context, the article takes the application of big data technology as a starting point, systematically discusses the application method of big data technology in 5G communication network, and studies the integration modeling and heterogeneous simulation of big data technology in 5G communication network based on deep learning. The simulation results show that the heterogeneous network improves big data by 6.6%. © 2022 IEEE

关键词： Heterogeneous networks

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modeling of Diverse Computing Requirements in Azure Task Traces

Modeling of Diverse Computing Requirements in Azure Task Tra...

引用

IEEE international conference on Communications (IEEE ICC)

作者： Ma, Mulei Gong, Chenyu Yang, Yang Gao, Yue Li, Kai ShanghaiTech Univ SIST Shanghai Peoples R China Terminus Grp Beijing Peoples R China Peng Cheng Lab Shenzhen Peoples R China Fudan Univ Shanghai Peoples R China

ISBN: (纸本)9798350333077

In the cloud data centers of large technology companies, there are various resources such as computing, communication and storage, which provide stable and quality-guaranteed services for subscribers. To get a comprehensive understanding of real load trends and to observe the characteristics of user tasks consuming resources, we analyzed the workload of Microsoft Azure Virtual Machines (VMs). Specifically, we analyzed the workload of all VMs in the Azure cluster within one month in 2017 and 2019. The existing public data provides complete information for each schema. Through data analysis and fitting, we revealed the consistency and difference in delay and memory usage of workload tasks during the time span from 2017 to 2019. In addition, we found the distribution characteristics of the task computing requirement, i.e., it conforms to a large scale exponential distribution superimposed with a small scale Sample function. We propose two modeling approaches to fit the 2017 and 2019 traces and find that the complex one is able to reduce the Sum of Squares due to Error (SSE) by 72.9% and 66.9% compared to the simple one. The task characteristics we discovered can help researchers understand the workload and provide a modeling basis for the simulation framework.

关键词： Azure workloads Trace analysis Computing modeling Delay CPU cores

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Forecast and analysis of Guangxi express delivery market based on big data analysis 24

Forecast and analysis of Guangxi express delivery market bas...

引用

5th international conference on Artificial Intelligence and Computer Engineering, ICAICE 2024

作者： Liu, Dalong Nanning University Guangxi Nanning China

ISBN: (纸本)9798400718007

With China's economic and Internet trade development, the logistics industry is increasingly important. The regional express business volume grows with economic development and e - commerce expansion, bringing both challenges and opportunities. This paper first describes the research status of domestic express delivery volume, including various forecasting methods' results. Then, it introduces SPSS, including its functions and advantages. Next, it elaborates on the SPSS operation flow in forecasting Guangxi's express business volume, from data import and arrangement to result interpretation. The SPSS prediction results show a high - fitting model and reasonable error range, suitable for prediction. Moreover, the results indicate that Guangxi's express delivery business volume will grow steadily from 2024 to 2026, and business demand will further increase. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Prediction models

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Application of Cloud Computing in Information Management System in the Era of big data 2

Application of Cloud Computing in Information Management Sys...

引用

2nd international conference on Computational modeling, simulation and data analysis, CMSDA 2022

作者： Miao, Xianghui Liaoning Dalian China

ISBN: (纸本)9798350345759

Cloud computing can provide long-term maintenance technology for enterprise management planning, and improve the operational efficiency of enterprises without increasing energy consumption. Therefore, this paper studies how to improve the information management efficiency of small and medium-sized enterprises by introducing cloud computing technology to build software application system. Cloud computing enterprise management system of big data adopts Saas mode of cloud computing, cloud server is created by Alibaba Cloud, and virtual cluster is created by hadoop to realize distributed storage and calculation of enterprise information data. The application design adopts J2EE technology, java language and SSH framework to establish the system. In this paper, a storage resource pool is built in the virtual cluster, and then different servers are used as storage shared resource pools of different resources. Cloud platform can control and manage all virtual machines. © 2022 IEEE.

关键词： big data

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