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检索条件"任意字段=Proceedings of the 2023 International Conference on Big Data Mining and Information Processing"

共 20019 条记录，以下是21-30 订阅

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Overview of spatial data management methods based on digital twins for airport construction 24

Overview of spatial data management methods based on digital...

引用

8th international conference on Electronic information Technology and Computer Engineering, EITCE 2024

作者： Sang, Hongxia Liang, Congshan Nie, Liangxue Zhao, Zihan Wang, Han Academy of Military Sciences Beijing China Geovis Technology Co. Ltd. Beijing China 93125 troops of PLA Jiangsu Xuzhou China

ISBN: (纸本)9798400710094

The command and resource scheduling capabilities of the airport serve as crucial support for rapid take-off and landing, as well as efficient aircraft operations, the future intelligent airport scheduling poses new requirements for airport construction, it is necessary to effectively manage and analyze the vast and complex airport spatial data. To this end, the digital twin spatial data management methods for airport construction are comprehensively studied, and three different types of storage systems, general parallel computing framework, multi source data association algorithm and real time data processing engine have developed in this article, that can realize the whole time and space multi-dimensional data fusion management and the association and fusion between multi-source heterogeneous data entities, solve the problem of managing and analyzing the huge spatial data of the airport, to provide technical support for space-air big data analysis and mining. © 2024 Copyright held by the owner/author(s).

关键词： data fusion

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ICICM 2024 - proceedings of the 2024 14th international conference on information Communication and Management

ICICM 2024 - Proceedings of the 2024 14th International Conf...

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14th international conference on information Communication and Management, ICICM 2024

ISBN: (纸本)9798400717475

The proceedings contain 8 papers. The topics discussed include: new information technologies and management: digitalization;a taxonomy for smart building data sensitivity;multi-flow process mining for comprehensive simulation model discovery;the microservice based architecture for IoT systems in agriculture and aquaculture;dominance analyses reduction in skyline query processing over data stream with data mining technique;user experience and information security implications of digital identity wallets;comprehensive technological model for monitoring personnel performance in coffee plantations through harvest container tracking and user id: optimizing harvest efficiency through real-time performance tracking in agricultural operations;and a flexible model for process and resource related analysis of document workflows.

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Automated execution algorithm for smart contracts in payment systems based on blockchain and big data 4

Automated execution algorithm for smart contracts in payment...

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4th international conference on Computer Vision, Application, and Algorithm, CVAA 2024

作者： Lin, Shuhong Ruan, Juanping Chen, Lihong Lin, Yishan Zou, Limei Pan, Liumei Fu Jian Yi Li Dian Li Ke Ji Co. Ltd. Fuzhou350001 China State Grid Info & Telecom Group Fuzhou350001 China

ISBN: (数字)9781510687622

ISBN: (纸本)9781510687615

Smart contracts, as one of the important applications of blockchain technology, provide the possibility for automated execution of payment systems. This article explores an automated execution algorithm for smart contracts in payment systems based on blockchain and big data technology to address the issues of low accuracy and efficiency in contract execution. This algorithm utilizes the immutability and decentralization features of blockchain to ensure the security and transparency of payment transactions. At the same time, combined with the powerful analytical capabilities of big data, deep mining of transaction data is carried out to achieve accurate prediction and risk management of transactions. The automated execution function of smart contracts further simplifies the transaction process, reduces human intervention, and improves the efficiency and reliability of payment systems. The experimental results show that through the proposed algorithm, the payment system can provide more secure, efficient, and personalized services, promoting the digital transformation and development of the financial transaction field. © 2025 SPIE.

关键词： Blockchain data modeling data processing Automation Computer security data storage Transparency data communications Statistical modeling Symmetric key encryption

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Resource Forecasting of Geographical Area Using big data Analytics 5th

Resource Forecasting of Geographical Area Using Big Data Ana...

引用

5th international conference on Innovative Product Design and Intelligent Manufacturing Systems, ICIPDIMS 2023

作者： Rejeti, Venkata Kishore Kumar Chandra, G. Rajesh Mounika, V. Thanveer, S. K. Thasleema Jyothika, V. Harika, Y. Anand, D. Department of CSE KKR & KSR Institute of Technology and Sciences Guntur India Department of CSE Koneru Lakshmaiah Education Foundation AP Vaddeswaram India

ISBN: (纸本)9789819767311

Prospective purchasers now deal with location ignorance in a certain area. Here, we want to set up a big data analytics system that can handle different kinds of data processing for a broad variety of uses. big data analytics (BDA) is an organized method for examining and spotting various relationships, patterns, and trends in a sizable amount of data. In this study, we use BDA to data resources where visualization and trend prediction are achieved by exploratory data analysis. Hadoop-based big data analytics has a useful function. We can extract previously undiscovered, valuable information from structured data by using the big data idea and data mining tools. Here, computer science and the municipality are working together to create a data mining process that will assist people seeking to learn specifics about a certain geographic location. This project may be produced with the aid of data on transportation, utilities, water supply, electricity supply, and other amenities. It is based on location data that our interface will analyses in the past and provide reports to the user. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： data Analytics

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Study on Classification Models Utilizing the Multilayer Perceptron Feature Selection and Ensemble Voting Classifier 24

Study on Classification Models Utilizing the Multilayer Perc...

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5th international conference on Computer Science and Management Technology, ICCSMT 2024

作者： Zhu, Xiaohang Fang, Huan Zhao, Xinli School of Mathematics and Big Data Anhui University of Science and Technology Anhui Huainan China School of Petroleum and Natural Gas Engineering Changzhou University Jiangsu Changzhou China

ISBN: (纸本)9798400709999

The existing classification prediction models increase in training time and cost with the increase in the number of features, significantly increase in computational complexity, and irrelevant or redundant features interfere with the model's learning process and affect the accuracy of classification prediction *** this study, a model combining the multilayer perceptron (MLP) feature selection with an ensemble voting classifier (MRS) is *** MLP feature selection method demonstrates superiority in screening important features, which can effectively extract key information from the original ***, the classification prediction performance of the model is further improved by feeding the filtered features into an ensemble voting classifier that incorporates Random Forest (RF) and Support Vector Machine (SVM).The model enhances the processing efficiency of classification problems in the field of data mining, especially in the context of confronting multi-featured and high-dimensional data, and more accurately performs classification ***, the accuracy of the model exceeds that of many other models is proved by the experiments, demonstrating its great potential in solving the classification and prediction problems in the field of data mining. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Random forests

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Why multilayer networks are needed for complex data analysis 2nd

Why multilayer networks are needed for complex data analysis

引用

2nd international conference on Next-Generation Computing and information Systems, ICNGCIS 2023

作者： Chakravarthy, Upendranatha Sharma The University of Texas at Arlington United States

ISBN: (纸本)9781032738659

We are on the cusp of holistically analyzing a variety of data being collected in every walk of life in diverse ways. For this, current analytics and science are being extended (big data Analytics/Science) along with new approaches to benefit humanity at large in the best possible way. These warrants developing and/or using new approaches, technological, scientific, and systems, in addition to building upon and integrating with the ones that have been developed so far. In the first part of the presentation, we introduce multilayer networks (or MLNs) and illustrate their elegance for modeling complex data sets from well-known applications (IMDb and DBLP). Flexibility of analysis comes from this approach to modeling. We will show a dashboard that we have developed for MLN analysis. In second part of this talk, we discuss situation analysis on videos using a novel approach. We extract contents and apply stream processing techniques. This approach has the potential to process videos in real-time and raise alerts for situations, such as Nancy Pelosi’s house break-in and Las Vegas shooting. © 2025 The Author(s).

关键词： data Analytics

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Edge computing and big data analysis integration framework in smart grid edge gateway

Edge computing and big data analysis integration framework i...

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2024 international conference on Physics, Photonics, and Optical Engineering, ICPPOE 2024

作者： Liu, Yuan Zhang, Wei Liu, Liguo Li, Xuehua Yang, Chenjie Wang, Yukui State Grid Information & Telecommunication Co. Ltd. Beijing branch Beijing China

ISBN: (数字)9781510689121

ISBN: (纸本)9781510689114

This study aims to enhance the data processing efficiency, real-time performance, and system reliability of smart grids by integrating edge computing and big data analysis in smart grid edge gateways. This study shows how advanced technologies like edge devices, edge computing nodes, big data analytics modules, and control systems can be utilized in a real grid setting to enhance operational efficiency and grid prediction ability through a comprehensive architecture design and implementation. In the research, we concentrated on data real-time processing, transmission mechanism, and system architecture design, and we validated the framework's efficiency with real cases. Studies indicate that the integration of edge computing and big data analysis in an organic manner can enhance the data processing abilities of the power grid, decrease delays, and offer robust backing for optimal power grid management. This framework is capable of satisfying the requirements of current smart grids, as well as possessing strong scalability and adaptability, providing a robust groundwork for the advancement of future smart grids. © 2025 SPIE.

关键词： Mobile edge computing

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Anomaly Detection in Enterprise information Systems Based on Deep Learning 24

Anomaly Detection in Enterprise Information Systems Based on...

引用

3rd international conference on Algorithms, data mining, and information Technology, ADMIT 2024

作者： Cheng, Luming Co. Ltd Chengdu China

ISBN: (纸本)9798400718120

This study presents an innovative anomaly detection framework for enterprise information systems, integrating deep learning and multi-model ensemble techniques. The framework has been evaluated on a large-scale real-world dataset, demonstrating superior performance. Experimental results show significant achievements in key metrics such as accuracy, F1 score, and AUC value. Compared to traditional methods, this framework exhibits notable improvements in detection precision and processing speed. It excels in identifying critical anomalies such as system crashes and network attacks, effectively reducing false positive and false negative rates. The framework also shows excellent scalability and adaptability, capable of handling new anomalies and dynamically changing system environments. The research findings provide robust support for enhancing the security and reliability of enterprise information systems, offering broad practical application prospects. © 2024 Copyright held by the owner/author(s).

关键词： Anomaly detection

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Integration and Performance Evaluation of big data Real-Time Stream processing Framework in Dynamic Tracking Analysis 4

Integration and Performance Evaluation of Big Data Real-Time...

引用

4th international conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Yang, Xiao-Mei Zhang, Ju-Ling Wu, Lei College of Information Management Xinjiang University of Finance and Economics Urumqi830012 China The Communist Youth League Committee Xinjiang University of Finance and Economics Urumqi830012 China

ISBN: (纸本)9798331523923

Real-time data processing, especially in the field of public opinion analysis, is an important branch of computer aided stream data processing. Therefore, this study proposes a new big data real-time stream processing framework, and evaluates its performance from the application scenario level. Firstly, an integrated real-time data stream processing framework is designed. This stable device enables efficient data capture, stream reorganization, and data export at high network rates. By introducing a joint optimization algorithm, the framework optimizes the generalization ability of data cluster analysis at the bottom of the data, thus improving the overall reliability of the framework. Secondly, this study focuses on the online public opinion analysis scenario. The model constructs a dynamic public opinion tracking model based on information evolution. The model simulates the propagation process of public opinion information on the network, and uses incremental learning and deep learning algorithms to conduct comprehensive public opinion analysis and risk warning. Finally, through the experimental analysis of the public data of social platforms (such as Facebook, Weibo, Twitter), this paper verifies the application effect of the framework in real-time public opinion risk warning. By training the model to predict the emotional content, the experimental results show that the accuracy of the model is 0.95, the recall rate is 0.97, and the result is satisfactory. © 2025 IEEE.

关键词： data accuracy

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Research on Intelligent mining Methods of University Innovation and Entrepreneurship Education Service data Based on Multimedia Internet of Things Technology 24

Research on Intelligent Mining Methods of University Innovat...

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7th international conference on Educational Technology Management, ICETM 2024

作者： Yu, Wei Nanning University Guangxi Nanning China

ISBN: (纸本)9798400717468

The massive amount of data directly leads to the complexity of the mining process, resulting in a longer time required for data mining. Therefore, this study designs an intelligent mining method based on multimedia Internet of Things technology for university innovation and entrepreneurship education service data. Firstly, utilizing web scraping technology to obtain massive amounts of data, and developing association rules between data based on their characteristics and mining needs;Then, using multimedia Internet of Things technology to intelligently screen the data, filtering out noise and irrelevant information;Finally, using visualization techniques, the mined data is presented in an intuitive and understandable manner. The comparative experimental results show that compared with traditional mining methods, the new method requires less time to mine data, indicating that the new method is more competitive in practical applications. © 2024 Copyright held by the owner/author(s).

关键词： Educational Service data

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