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作者： Rashed, Tarek Colorado Technical University

学位级别：D.C.S., Doctor of Computer Science

Developing BDVA tools using Open Source data Visualization (OSDV) has become a popular approach for creating software tools due to the fact that it can be done at lower costs and at a faster pace. Embedding OSDV to develop Visual Analytics (VA) tools in order to work effectively with big data platforms is challenging. Thus, software production might not be valuable without establishing an effective combination of strategy, technology, and skills. This study aimed to learn from big data specialists' and developers’ standpoints how strategies are implemented to discover skills and methods for effectively leveraging OSDV to advance the development of BDVA tools. Through a qualitative exploratory approach, 10 big data professional were interviewed. Firstly, the study findings indicated that developing VA tools via OSDV requires many technologies. These include developing libraries to integrate data from various sources and interactively presenting data in various visual diagrams. Secondly, developing any successful BDVA requires a combination of accurate and clean data, analytical tasks, and graph types. Lastly, the study revealed that many existing OSDV, which are used to generate dashboards and charts, have a lack of technical documentations. Although this study explored the strategies, the findings motivate research recommendations: (a) identifying the best strategies to achieve interactive mechanisms, (b) exploring the capabilities of OSDV in developing visualization tools for the healthcare data, (c) exploring professionals' experiences regarding open-source data quality management tools, and (d) exploring experiences of practitioners and then developing a strategy that might mitigate the risks of reusing ***: Open-Source Libraries, big data, Visual Analytics, Visualization Tools, big data Tools, big data technology.

关键词： big data technology big data big data tools Open-source libraries Visual analytics Visualization tools

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Construction of an Intelligent Mental Health Assessment System Based on big data Algorithms

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Procedia Computer Science 2024年 247卷 1102-1109页

作者： Fangrui Men Digital Marketing Industry College Shandong Institute of Commerce and Technology Jinan 250103 Shandong China

College students are in the stage of transitioning from teenagers to adults, and there are many problems that need to be adapted and adjusted in life, learning, and interacting with others. If these issues are not handled properly, it would lead to many mental health problems that affect normal learning and life. The paper use the data mining algorithm of big data. Through systematic intelligent evaluation, it can gain an overall understanding of the mental status of students. For students, it is effective to have a clear understanding of their mental health status, and to communicate with their home and school in a timely manner regarding their psychological problems. Early treatment of mental health can be more effective in completing rehabilitation; For universities, they can have a clear understanding of each student's psychological situation to avoid some difficult situations. At the same time, universities can timely intervene and treat students with psychological problems. This paper use data mining algorithms to build an intelligent evaluation system for mental health. Through research, it is found that the accuracy of this method is 95% at the lowest and 97% at the highest. This method can comprehensively and objectively describe the changing characteristics of college students’ mental health, and the results of mental health intelligence assessment are stable, providing valuable reference for their mental health management.

关键词： big data technology data Mining Algorithm Mental Health Intelligent Evaluation System

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Analysis and research on the creation of smart supply chain system based on information technology 24

Analysis and research on the creation of smart supply chain ...

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6th Asia Pacific Information technology Conference (APIT)

作者： Ji, Wenjun Maanshan Vocat & Tech Coll Maanshan 243031 Anhui Peoples R China

ISBN: (纸本)9798400716218

Smart supply chain system is one of the important competitiveness and strategic advantages of enterprises in the digital era. Through the use of modern information technology, it realizes the comprehensive digitalization, intelligence and high efficiency of supply chain management. This paper first analyzes the connotation and characteristics of the smart supply chain, then analyzes the core information technology of the construction of the smart supply chain, and discusses the architecture design of the smart supply chain system. Finally, it analyzes the application characteristics of the smart supply chain platform based on information technology.

关键词： big data technology Internet of Things technology information technology smart supply chain system

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A big-data-based recurrent neural network method for forest energy estimation

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SUSTAINABLE ENERGY TECHNOLOGIES AND ASSESSMENTS 2023年 55卷

作者： Song, Yang Wang, Youzhi Guizhou Univ Finance & Econ Coll Econ Guiyang 550025 Gui Zhou Peoples R China Guizhou Univ Finance & Econ Coll Publ Management Guiyang 550025 Gui Zhou Peoples R China

In this paper, the recurrent neural network algorithm of big data is used to conduct in-depth research and analysis on the estimation of forest energy, and the corresponding forestry construction and biomass energy industry development are proposed. Firstly, by introducing a non-negative variable, the bounded constrained underdetermined linear system is transformed into a time-varying system consisting of linear and non-linear equations. Meanwhile, the actual situation of industrial raw material supply of forest biomass is explored by considering the technical level of collection, transportation, and storage. Then, three different alternative scale scenarios are designed to explore the development dynamics of the forest biomass industry in Guizhou under different levels of development and to propose optimization schemes and policy recommendations accordingly. Finally, the simulation results show that in terms of energy efficiency, economic benefits, and environmental benefits, the proposed mehtod can achieve carbon dioxide emission reduction benefits of about 46.17% and increase the economic benefits of carbon dioxide trading by 35.69%.

关键词： big data technology Recurrent neural network Forest energy estimation Method analysis

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Flight test data processing and analysis platform based on new generation information technology Design and Application 3

Flight test data processing and analysis platform based on n...

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3rd International Conference on Sensing, Measurement and data Analytics in the Era of Artificial Intelligence, ICSMD 2022

作者： Deng, Guobao Xie, Moxing Feng, Can Liu, Tao Zha, Xiaowen COMAC Flight Test Center Flight-Test Instrumentation Department Shanghai China

ISBN: (纸本)9781665492812

Flight test data is the core product of flight test, which has the characteristics of many parameters, large data volume, diverse data types, random data changes, high demand for data timeliness, and high data value. It serves aircraft design, manufacturing, flight test, operation, etc. stage. In the traditional data management mode, the flight test data management is mainly in the form of text files, which is difficult to quickly respond to the application requirements of PB-level stored flight test data and give full play to the value of flight test data. Based on the characteristics of flight test data, introduce cloud computing technology and big data technology, design flight test data processing and analysis platform, build a spark-based flight test data processing system, MPP-based flight test data warehouse, cloud native flight test data service system, and B/S-based flight test data analysis software and other functional modules have been successfully applied in the flight test of a domestic civil aircraft. Provide services for flight test engineers, aircraft designers and project engineers, improving the efficiency of flight test data management and the application value of flight test data. © 2022 IEEE.

关键词： big data technology cloud computing technology flight test data flight test data analysis software flight test data processing system flight test data service system flight test data warehouse

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Research on the Application of AIS data Analysis technology in Shipping

International Core Journal of Engineering

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International Core Journal of Engineering 2024年第1期10卷 295-300页

作者： Taizhi Lv Enze Wu Yingying Chen

Due to the popularity of Automatic Identification System (AIS), the shipping industry has accumulated a large amount of data, providing rich resources for data analysis. AIS data analysis has been widely used in many fields such as route optimization, water traffic management, environmental controlling, economics and trade, and is an indispensable part of the modern shipping industry. As technology develops, its application scope and depth will continue to expand. This paper summarizes the main areas of application of AIS data analysis technology in the shipping industry. It analyzes how AIS can improve the efficiency, safety and environmental sustainability of the shipping industry from the fields of maritime management and environmental protection. It also provides insight and decision-making for the shipping market. Provided important support.

关键词： Automatic Identification System (AIS) Maritime Management big data technology Ship Trajectory Water Transportation

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Research on the Teaching Reform of “big data Analysis and Visualization” Course for College Students

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Journal of Contemporary Educational Research 2024年第8期8卷 59-63页

作者： Yansong Yang Xiaodan Chen Smart City College Beijing Union UniversityBeijing 100101China

Under the background of the big data era,the education of big data majors is undergoing a profound teaching reform and *** the increasing role of big data technology in analysis and decision-making,updating and expanding the teaching content of big data majors has become particularly *** the era of big data,modern enterprises have put forward new and higher demands for big data talents,which not only include traditional data analysis skills but also knowledge of data visualization and information *** address these challenges,big data education needs to reform and innovate in the development and utilization of teaching content,methods,and *** paper proposes teaching models and reform methods for big data majors and analyzes corresponding teaching reforms and innovations to meet the requirements of the new development of big data *** traditional classroom teaching method is no longer sufficient to meet the learning needs of students,and more dynamic and interactive teaching methods,such as case studies,flipped classrooms,and project-based learning,are becoming increasingly *** innovative teaching methods can more effectively cultivate students’practical operation skills and independent thinking while allowing them to better learn advanced knowledge in a real big-data *** addition,the paper also discusses the construction of big data processing and analysis platforms,as well as innovative teaching management and evaluation systems to improve teaching quality.

关键词： data science big data technology Course Teaching reform

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A FAULT MONITORING SYSTEM FOR MECHANICAL AND ELECTRICAL EQUIPMENT OF SUBWAY VEHICLES BASED ON big data ALGORITHMS

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SCALABLE COMPUTING-PRACTICE AND EXPERIENCE 2024年第5期25卷 3376-3387页

作者： Li, Geng Li, Ya Bi, Hongxue Zhengzhou CSCEC Shenzhen Railway Rail Transit Co L Zhengzhou 450000 Peoples R China Zhengzhou Railway Vocat & Tech Coll Zhengzhou 450000 Peoples R China

This paper uses big data technology to extract the electromechanical fault characteristics of metro vehicles and analyze the current situation under different fault conditions to ensure the operation quality and safety of metro operations. It also establishes a simulation model to simulate the current waveform of metro vehicles under different fault conditions and analyze the fault phenomenon. The simulation test results show that: (1) The current waveform of a single transistor with the hard fault is compared with the simulated current waveform under a normal state. The upper part of the A phase current waveform is lost when T1 fails. When T2 fails, the current waveform in the lower half of the C phase current is lost. When T3 fails, the upper half of the B phase current waveform is lost. (2) The current waveform in the hard fault's upper and lower bridge arms will have phase loss. In the T25 fault, the C phase current is completely lost. In the T14 fault, the phase A current waveform is completely lost. In the T36 fault, the phase B current is completely lost. (3) The current waveform of a single transistor with a soft fault is complete, but the overall current amplitude is reduced. When a T1 fails, the A phase current tends to rise first and then fall. Compared to normal, the amplitude of the current decreases, and the peak decreases slightly. (4) The current values of phase B and phase C of the two transistors on the same bridge above and below the soft fault are mostly the same. The phase A current output value decreases in both the positive and negative half cycles. This paper aims to improve the monitoring ability of the monitoring system of electromechanical equipment of metro vehicles, which plays a specific role in maintaining the safety of subway operations and improving the quality of subway operations.

关键词： big data technology metro vehicles electromechanical equipment monitoring systems simulation

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data-driven smart sustainable urbanism: the intertwined societal factors underlying its materialization, success, expansion, and evolution

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GEOJOURNAL 2021年第1期86卷 43-68页

作者： Bibri, Simon Elias Norwegian Univ Sci & Technol Dept Comp Sci Sem Saelands Veie 9 NO-7491 Trondheim Norway Norwegian Univ Sci & Technol Dept Architecture & Planning Alfred Getz Vei 3Sentralbygg 15th Floor NO-7491 Trondheim Norway

Visions of future advances in science and technology (S&T) inevitably bring with them wide-ranging common visions on how societies, and thus cities as social organizations, will evolve in the future and the immense opportunities this future will bring. This relates to the role of science-based technology in modern society. The focus here is on big data science and analytics and the underpinning technologies as an instance of S&T and its role in advancing sustainability in modern cities. This relates to what has been dubbed data-driven smart sustainable urbanism. However, there is a little understanding about how it has emerged and why it has become institutionalized and interwoven with politics and policy-urban dissemination. Therefore, this paper examines the intertwined societal factors underlying its materialization, success, expansion, and evolution, as well as critically discusses urban science and big data technology as social constructions in terms of their inherent flaws, limits, and biases. This paper argues that data-driven smart sustainable urbanism is shaped by socio-cultural and politico-institutional structures. And it will prevail for many years to come given the underlying transformational power of big data science and analytics, coupled with its legitimation capacity associated with the scientific discourse as the ultimate form of rational thought and the basis for legitimacy in knowledge-making and policy-making. This paper also argues that there is a need for re-casting urban science in ways that reconfigure the underlying epistemology to recognize the complex and dynamic nature of smart sustainable cities, as well as for re-casting them in ways that re-orientate in how they are conceived.

关键词： data-driven smart sustainable urbanism Smart sustainable cities big data technology datafication Urban science data science Social constructions Sustainable development Science technology and society

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Construction of an Integrated Student Management Platform in a Smart Campus Environment

Construction of an Integrated Student Management Platform in...

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2024 International Conference on Innovative Design and Intelligent Manufacturing, IDIM 2024

作者： Li, Haitao Changchun Humanities and Sciences College Changchun130117 China

ISBN: (纸本)9781643685427

In order to comprehensively improve the efficiency of student management in colleges and universities, a comprehensive student management platform in a smart campus environment is proposed. From the technical level, we deeply explore the architecture design of the student management platform based on big data technology, and elaborate on the related technologies and algorithms of the student management platform architecture. The platform test results show that: after adding 500 virtual users at one time in the testing tool LoadRunner, setting the functional operation calling node to all functional test cases, using Random mode to execute online the automated scripts of functional test cases imported in advance, the maximum response time is 2.85s, which is lower than the 3-second response time indicator with clear performance requirements and test indicators. Conclusion: Provide reference suggestions for the application of big data analysis technology in college student management under the background of the big data era. © 2024 The Authors.

关键词： big data era big data technology smart campus student management platform

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