Journal articles on the topic 'Big data training'
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Минязова, Е. Р. ""Big Data" and personalized training." Higher education today, no. 5-6 (July 18, 2022): 41–45. http://dx.doi.org/10.18137/rnu.het.22.05-06.p.041.
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Рассмотрена значимость анализа «больших данных» для современного образования, возможности работы с ними на примере функционирования образовательной платформы. Показаны перспективы применения технологий big data в персонализированном онлайн-обучении, а также риски применения анализа «больших данных». The article describes the importance of big data analysis for modern education. The possibilities of working with big data on the example of educational platform have been considered. Prospects of big data technologies in personalized online learning, as well as risks of big data analysis are considered.
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Scaife, Anna M. M., and Sally E. Cooper. "The DARA Big Data Project." Proceedings of the International Astronomical Union 14, A30 (August 2018): 569. http://dx.doi.org/10.1017/s174392131900543x.
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AbstractThe DARA Big Data project is a flagship UK Newton Fund & GCRF program in partnership with the South African Department of Science & Technology (DST). DARA Big Data provides bursaries for students from the partner countries of the African VLBI Network (AVN), namely Botswana, Ghana, Kenya, Madagascar, Mauritius, Mozambique, Namibia and Zambia, to study for MSc(R) and PhD degrees at universities in South Africa and the UK. These degrees are in the three data intensive DARA Big Data focus areas of astrophysics, health data and sustainable agriculture. The project also provides training courses in machine learning, big data techniques and data intensive methodologies as part of the Big Data Africa initiative.
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Abdullateef Omitogun, Abdullateef Omitogun, and Khalid Al-Adeem Abdullateef Omitogun. "Auditors’ Perceptions of and Competencies in Big Data and Data Analytics: An Empirical Investigation." International Journal of Computer Auditing 1, no. 1 (December 2019): 092–113. http://dx.doi.org/10.53106/256299802019120101005.
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<p>This study presents evidence on practicing auditors’ perceptions of and competencies in applying big data and data analytics to audit engagements. An electronic questionnaire distributed to accountants shows that auditors have good information technology skills and are well-acquainted with big data and data analytics. However, they lack relevant technical skills and are unfamiliar with related data analysis tools, excluding Excel. The results reveal 64.71% of accountants have not attended any training on big data and data analytics, while 31.37% plan to enhance related knowledge. Auditors need to obtain training on substantive audit risk assessments using big data and data analytics.</p> <p> </p>
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Wang, Yiting, and Le Yu. "Multisource Analysis of Big Data Technology: Accessing Data Sources for Teacher Management of Sports Training Institutions." Mobile Information Systems 2022 (August 13, 2022): 1–12. http://dx.doi.org/10.1155/2022/5115184.
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In the information age, “mobile Internet,” “cloud computing,” “Internet of Things,” and “data mining” concepts are emerging at the same time, as well as other fields of related data-based applications. The mobile application will be born as a result. Therefore, in the information age, big data, which involves information in a specific key or specialized field, has gradually begun to receive a lot of attention in recent years. In 2011, the US consulting firm McKinsey and Company first proposed the arrival of the “era of big data” and in August 2015 in China’s State Council issued a notice of action outline “to promote the development of big data.” Meanwhile, big data has gradually become an important factor in driving national reform and innovation, promoting scientific and technological progress, improving the way society is managed, and guiding changes in education and research. Big data is driving a very influential shift in thinking in an era where big data is changing the way we live, becoming the way we understand the world, and gradually becoming the source of new inventions and services. At the same time, the rapid development of big data technology for physical education teachers needs big data for management and training and other institutional managers to provide more effective ways and means of education management, but up to now, the status of big data for management is still another serious challenge, sports and training and other institutions of big data and processing process of data nonintelligent, nonclosed-loop processing, data nonlinked processing, etc. Many problems are also still very obvious. According to the new characteristics of sports big data refinement management, the current situation of sports professional training institutions teacher management, combined with sports training institutions to find some more practical sports training institutions teachers big data management methods can effectively improve the efficiency of management, teacher team building, strengthen sports training institutions to improve the quality of teaching teachers, and promote the overall quality of students have a positive impact. In this paper, we combine the characteristics of “big data” and the construction of teachers in sports training institutions, and put forward some suggestions on how to improve the level of teachers in sports training institutions in the era of big data and conclude that the construction of teachers in sports training institutions should seize the key era now and enter the “|big data era.” We conclude that the construction of teachers in sports training institutions should seize the critical era and enter the “big data era,” so as to rely on science and technology to improve the construction system of teachers in sports training institutions.
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Wang, Huiqin. "College Physical Education and Training in Big Data: A Big Data Mining and Analysis System." Journal of Healthcare Engineering 2021 (November 30, 2021): 1–8. http://dx.doi.org/10.1155/2021/3585630.
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Recently, big data has been broadly used as a research method in all aspects of analysis, prediction, and evaluation. The application of big data to college students’ physical education plays a significant role in encouraging the completion of physical education at various levels. The application of the Internet and the advent of smartphones impact the way college students participate in physical exercise. At present, more and more students begin to participate in sports, and students’ demand for physical training is increasing. During physical education training, a lot of data is generated every moment because of various actions and behaviors. Due to technical limitations, these data were not effectively collected and applied. In this environment, the development and management of sports data mining systems have become more and more important. This paper designs an intelligent big data system for college physical education training. The study mainly focuses on data decentralization, lack of data talents, insufficient technical support, and low utilization of venues in physical education. While designing a big data system, the data is collected based on ease of data collection, and a response framework with excellent performance in storing analytical data is selected. The design and management of this system have a certain significance for the improvement and optimization of current college physical education training.
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Jinhui, Zheng, Wang Sheng, Zheng Jinhong, Cai Guoliang, Cai Zhiqiang, and Du Yuntao. "Analysis on Survey Data of Special Physical Training for Skiers in Summer Training Based on Big Data." Mobile Information Systems 2021 (December 28, 2021): 1–6. http://dx.doi.org/10.1155/2021/3024089.
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Due to the geographical and natural conditions, the development of skiing events is more resistant in China, and the training venues, methods, and concepts are insufficient, making it difficult for Chinese skiers to make some progress and aspire to the highest peak in this field. The purpose of this study is to explore and analyze the survey data of the professional physical training of skiers in summer training based on big data. Big data is employed to investigate and analyze the special physical training of skiers in summer training. Based on the data of professional physical training of skiers in summer training under big data, the current situation of skiers in summer training is examined, and the limitations are compared to improve the traditional physical training of skiers. Results show that the special physical training of skiers based on big data is more feasible in summer training, and the improvement of training effect is more obvious than traditional physical training. The training effect of the proposed method can more effectively solve the difficulties in summer training for skiers and understand the essentials of the action.
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Serik, M., G. Nurbekova, and J. Kultan. "Big data technology in education." Bulletin of the Karaganda University. Pedagogy series 100, no. 4 (December 28, 2020): 8–15. http://dx.doi.org/10.31489/2020ped4/8-15.
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The article discusses the implementation of big data in the educational process of higher education. The authors, analyzing a large amount of data, referring to the types of services provided by e-government, indicate that there are many pressing problems, many services are not yet automated. In order to improve the professional training of teachers of Computer Science of the L.N. Gumilyov Eurasian National University, educational programs and courses have been developed 7M01514 — «Smart City technologies», «Big Data and cloud computing» and 7М01525 — «STEM-Education», «The Internet of Things and Intelligent Systems «on the theoretical and practical foundations of big data and introduced into the educational process. The arti-cle discusses several types of programs for teaching big data and analyzes data on the implementation of big data in some educational institutions. For the introduction and implementation of special courses in the educational process in the areas of magistracy in the educational program Computer Science, the curriculum, educational and methodological complex, digital educational resources are considered, as well as hardware and software that collects, stores, sorts big data, well as the introduction into the educational process of theoretical foundations and methods of using the developed technical and technological equipment.
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Qu, Qingling, Meiling An, Jinqian Zhang, Ming Li, Kai Li, and Sukwon Kim. "Biomechanics and Neuromuscular Control Training in Table Tennis Training Based on Big Data." Contrast Media & Molecular Imaging 2022 (August 10, 2022): 1–10. http://dx.doi.org/10.1155/2022/3725295.
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Thinking of big data as a collection of huge and sophisticated data sets, it is hard to process it effectively with current data management tools and processing methods. Big data is reflected in that the scale of data exceeds the scope of traditional volume measurement, and it is difficult to collect, store, manage, and analyze through traditional methods. Analyzing the biomechanics of table tennis training through big data is conducive to improving the training effect of table tennis, so as to formulate corresponding neuromuscular control training. This paper mainly analyzes various indicators in biomechanics and kinematics in table tennis training under big data. Under these metrics, an improved decision tree method was then used to analyze the differences between athletes trained for neuromuscular control and those who did not. It analyzed the effect of neuromuscular control training on the human body through different experimental control groups. Experiments showed that after nonathletes undergo neuromuscular control training, the standard rate of table tennis hitting action increases by 10% to 20%, reaching 80%. The improvement of athletes is not very obvious.
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Lane, Julia. "BIG DATA: THE ROLE OF EDUCATION AND TRAINING." Journal of Policy Analysis and Management 35, no. 3 (May 10, 2016): 722–24. http://dx.doi.org/10.1002/pam.21922.
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Wei, Jingwei. "Study and application of computer information big data in basketball vision system using high-definition camera motion data capture." Journal of Physics: Conference Series 2083, no. 4 (November 1, 2021): 042003. http://dx.doi.org/10.1088/1742-6596/2083/4/042003.
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Abstract Contemporary computer big data technology is developing rapidly, and it has played a role in promoting the intelligent development of sports. Based on this research background, the thesis revolves around the application of computer big data artificial intelligence in the field of basketball training. The study found that computerized big data in basketball training and teaching is mainly reflected in the following aspects: high-definition camera motion data capture, player analysis to obtain assisted training system, etc. The final paper uses a basketball vision system as a case study to analyze specific applications of big data for computer information.
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Rodolfa, Kit T., Adolfo De Unanue, Matt Gee, and Rayid Ghani. "An Experience-Centered Approach to Training Effective Data Scientists." Big Data 7, no. 4 (December 1, 2019): 249–61. http://dx.doi.org/10.1089/big.2019.0100.
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Argueso, Cristiana T., Sarah M. Assmann, Kenneth D. Birnbaum, Sixue Chen, José R. Dinneny, Colleen J. Doherty, Andrea L. Eveland, et al. "Directions for research and training in plant omics: Big Questions and Big Data." Plant Direct 3, no. 4 (April 2019): e00133. http://dx.doi.org/10.1002/pld3.133.
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Nie, Xueling. "Research on Training Mode of Big Data Marketing Talents." International Journal of Education and Humanities 4, no. 3 (September 19, 2022): 38–40. http://dx.doi.org/10.54097/ijeh.v4i3.1650.
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With the advent of the information age, the demand for data- oriented marketing talents is increasing rapidly, and the training objectives and modes of big data marketing are also the focus of education research. Through in-depth interviews with representative enterprises, NVIVO software is used to conduct in-depth interviews and in-depth analysis of the big data marketing in the talent demand for extraction and summary.
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Hao, Long, Juncai Zhi, Wei Zhu, and Limin Zhou. "Research on Badminton Player’s Step Training Model Based on Big Data and IoT Networks." Security and Communication Networks 2022 (February 26, 2022): 1–9. http://dx.doi.org/10.1155/2022/1972389.
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In view of the poor effect of badminton players’ physical fitness and response training, this paper puts forward the construction method of badminton players’ pace training model based on big data, constructs badminton players’ pace training index by combining big data technology, optimizes the pace training evaluation algorithm, and puts forward the corresponding Badminton Players’ pace training method to achieve the model design goal. Finally, experiments show that the badminton athlete’s pace training model based on big data has high practicability in the process of practical application and fully meets the research requirements.
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Hopp, Armin. "How to drive corporate training with big learner data." Strategic HR Review 14, no. 3 (June 8, 2015): 113–14. http://dx.doi.org/10.1108/shr-03-2015-0022.
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Hautop Lund, Henrik, Yan-Xin Liu, and Massimiliano Leggieri. "Body and Brain Training with Big Data and AI." Proceedings of International Conference on Artificial Life and Robotics 25 (January 13, 2020): 6–9. http://dx.doi.org/10.5954/icarob.2020.is2-1.
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Olga Yurievna, Bulatova. "USING BIG DATA IN TRANSPORT INFRASTRUCTURE." World of transport and technological machines 75, no. 4 (2021): 105–11. http://dx.doi.org/10.33979/2073-7432-2021-75-4-105-111.
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Presents the process of making strategic decisions based on the use of big data. A plan for op-timizing the functioning of the transport system using big data is also presented, which simplifies the implementation of the project: data integration, the launch of pilot projects, the creation of new tools and training of personnel take place in the context of a clear vision of a specific goal.
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Ivanov, Sergey, and Mykola Ivanov. "Marketing forecasting based on Big Data information." SHS Web of Conferences 107 (2021): 05002. http://dx.doi.org/10.1051/shsconf/202110705002.
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In the paper discusses the use of big data as a tool to increase data transfer speed while providing access to multidimensional data in the process of forecasting product sales in the market. In this paper discusses modern big data tools that use the MapReduce model. The big data presented in this article is a single, centralized source of information across your entire domain. In the paper also proposes the structure of a marketing analytics system that includes many databases in which transactions are processed in real time. For marketing forecasting of multidimensional data in Matlab, a neural network is considered and built. For training and building a network, it is proposed to construct a matrix of input data for presentation in a neural network and a matrix of target data that determine the output statistical information. Input and output data in the neural network is presented in the form of a 5x10 matrix, which represents static information about 10 products for five days of the week. The application of the Levenberg-Marquardt algorithm for training a neural network is considered. The results of the neural network training process in Matlab are also presented. The obtained forecasting results are given, which allows us to conclude about the advantages of a neural network in multivariate forecasting in real time.
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Dan, Songjian. "Teacher Intelligence Training Based on Big Data and Artificial Intelligence." International Journal of e-Collaboration 18, no. 3 (May 1, 2022): 1–11. http://dx.doi.org/10.4018/ijec.307137.
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The purpose of this paper is to improve the theoretical system of teacher's intelligent training, build a teacher's intelligent training platform, build an intelligent training course resource and establish a teacher's intelligent training mechanism. This article mainly uses the methods of investigation and interviews to analyze the status quo of teachers' professional development, existing problems, and research needs. The results of the questionnaire survey showed that 46.7% of participants were in favor of smart training, but there were also 70 people who believed that the biggest difficulty in smart training was the lack of a relatively fixed and effective platform. Therefore, it is necessary to design an intelligent training platform to provide a good platform for teachers to learn.
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Zhang, Xining. "Big Data Model of Higher Education Online Teaching Based on Intelligent Algorithm." Mobile Information Systems 2022 (August 25, 2022): 1–11. http://dx.doi.org/10.1155/2022/2492952.
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With the continuous development of society, the demand for processing large-scale data in many fields is increasing. Traditional processing training techniques have many limitations for big data analysis applications. Therefore, how to transform big data into general-purpose information becomes particularly important. This research mainly discusses the big data model analysis of higher education online teaching based on intelligent algorithms. The process of the experiment is to access how trainers interact or receive information stimulation in videos and courseware and how to cause relatively lasting changes in cognitive behavior. From the experimental research, we discovered the law of practical training and finally provided personalized teaching support services according to the needs and abilities of the trainers. On the other hand, the online training algorithm for big data analysis is studied, the methods needed to solve the big data mining task are discussed, and the online course training is recommended in many ways. Experimental data show that the algorithm of large-scale online training behavior data analysis on the behavior analysis results of online trainers is conducive to the improvement of online trainers’ learning efficiency. The experimental results show that the algorithm of large-scale online training behavior data analysis can show good model analysis performance, which is conducive to the prediction of the training personnel, and the prediction accuracy reaches about 90%. It is found that the algorithm that implements large-scale online training behavior data analysis can effectively categorize the relationship between the trainee’s visits. Through innovative data analysis methods, fast, efficient, and timely analysis of big data streams is realized.
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Dong, Xianfeng. "Physical Training Information System of College Sports Based on Big Data Mobile Terminal." Mobile Information Systems 2021 (August 27, 2021): 1–7. http://dx.doi.org/10.1155/2021/4109794.
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The development of information technology is changing all walks of life. People’s health problem is more and more prominent; people begin to talk about the reform of college sports training. Sports no longer rely on individual games, but on the comprehensive strength of science and technology competition. The fierce competition for Olympic gold medal in modern competitive sports is largely due to the competition of scientific and technological strength of various countries. China has also conducted a lot of research on sports information and made some achievements. Through the investigation, we know that, at present, the provincial level sports teams have established the relevant sports training information management system, which is very effective. The latest scientific and technological achievements are combined with sports to establish the university sports information system. The purpose of this paper is to analyze the university sports physical training information system based on big data mobile terminal, study the big data embedded system, improve the effect of sports skills training, and meet the social demand for high skilled sports talents. This paper uses the literature method, experimental investigation method, and big data spectral clustering algorithm-related experiments to study the advantages of big data and uses the value of big data and embedded system model to study the university sports physical fitness training information system based on big data mobile terminal. The results show that 40.6% of college students spend more time in physical exercise, based on the application of big data embedded system in college sports training; it is of great significance to arrange sports training methods to improve students’ sports training performance.
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Малахов, Владислав Валерьевич, and Лариса Германовна Смышляева. "BIG DATA TECHNOLOGIES AS A MEANS OF IMPROVING THE EFFECTIVENESS OF TRAINING SESSIONS IN SVE." Pedagogical Review, no. 4(44) (August 1, 2022): 72–80. http://dx.doi.org/10.23951/2307-6127-2022-4-72-80.
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В статье представлены результаты сравнительного анализа понятий «человеческий потенциал» и «человеческий капитал» в контексте рассмотрения актуальности и специфики использования технологий больших данных в образовании. Материалы статьи подготовлены на основе методов теоретического анализа, Big Data, наблюдения, анализа продуктов деятельности, тестирования обучающихся для выявления их способностей, сравнения и аналогии. Актуализированы возможности больших данных как средства повышения эффективности образовательных практик. Аргументирована целесообразность применения больших данных в учебном процессе среднего профессионального образования, ориентированных на развитие человеческого потенциала. Дано описание особенностей организации и результатов опытно-экспериментальной работы, проведенной на базе ОГБПОУ «Томский государственный педагогический колледж», по апробации авторского видения возможности применения технологий больших данных в практике учебной работы. Доказательно обосновано повышение эффективности учебного занятия профессиональной образовательной организации при использовании Big Data в контексте обеспечения условия для самореализации каждого обучающегося. Заданы ориентиры развития опыта применения технологий больших данных для обогащения образовательных контекстов антропоцентрированной направленности. The results of a comparative analysis of the concepts of “human potential” and “human capital” in the context of considering the relevance and specifics of the use of Big Data technologies in education are presented. The materials of the article are prepared based on the use of methods of theoretical analysis, Big Data, observation, analysis of products of activity, testing of students to identify their abilities, comparison and analogy. The possibilities of big data as a means of increasing the effectiveness of educational practices are updated. The expediency of using Big Data in the educational process of secondary vocational education focused on the development of human potential is argued. The description of the features of the organization and the results of experimental work carried out on the basis of the Tomsk State Pedagogical College on the approbation of the author’s vision of the possibility of using Big Data technologies in the practice of educational work is given. The increase in the effectiveness of the training session of a professional educational organization when using Big Data in the context of providing conditions for the self-realization of each student is proved. The guidelines for the development of the experience of using Big Data technologies to enrich educational contexts of an anthropocentric orientation are set.
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Song, Haiyan, Yao Ma, and Hongwei Chen. "Health Promotion Effects of Sports Training Based on HMM Theory and Big Data." Applied Bionics and Biomechanics 2022 (May 5, 2022): 1–10. http://dx.doi.org/10.1155/2022/6110247.
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In order to better analyze human health status and guide people to carry out reasonable physical training, this paper puts forward the construction method of human health status evaluation model after sports training based on big data. Firstly, the characteristic information of human health status after sports training is collected based on big data technology, and the evaluation index and evaluation algorithm of human health status after sports training are constructed. The evaluation system of human health status after sports training is constructed. Finally, the experiment proves that the proposed evaluation model of human health status after sports training based on big data has high practicability in the process of practical application and fully meets the research requirements.
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Kasmanto, Rony. "Analisis pelatihan online teknis big data menggunakan data logger Moodle." Jurnal Penelitian Ilmu Pendidikan 13, no. 2 (October 31, 2020): 137–46. http://dx.doi.org/10.21831/jpipfip.v13i2.29419.
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AbstrakHasil pengamatan perilaku yang dilakukan pada peserta pelatihan daring dapat dijadikan bahan evaluasi untuk penyelenggaraan pelatihan pada masa yang akan datang menjadi lebih optimal. Penelitian ini bertujuan untuk mengetahui perilaku peserta, pengajar dan panita dalam proses pembelajaran dengan keseluruhan daring. Pendekatan penelitian adalah statistik deskriptif dengan menggunakan data logs report pada Learning Management System (LMS) Pusdiklat BMKG. Objek penelitian adalah 50 peserta pelatihan teknis big data yang berasal dari unit pelaksana teknis di BMKG, 13 pengajar dan 2 panitia. Pelatihan diselenggarakan di Pusat Pendidikan dan Pelatihan BMKG pada 28 Oktober sampai dengan 9 Desember 2019. Hasil penelitian menunjukkan bahwa perilaku pelaku pelatihan memiliki aktivitas yang signifikan hanya pada momen-momen tertentu. Sistem LMS mencatat beberapa kejadian: 1) terjadi peningkatan aktivitas peserta, ketika mendekati tenggang waktu pengumpulan semua tugas yaitu pada hari senin dan pada saat pelaksanaan post-test; 2) terjadi peningkatan aktivitas pengajar ketika mendapat peringatan dari panitia untuk pemberian grading terhadap semua tugas-tugas yang dikumpulkan peserta; 3) terjadi peningkatan aktivitas panitia pada saat agenda pembelajaran dimulai dan ketika pelaksanakan post-test. Analysis of big data technical online training using Moodle logger dataThe results of behavioral observations conducted on online trainees can be used as evaluation material for the implementation of future training to be more optimal. This research aims to find out the behavior of participants, teachers, and organizers in the process of full online learning. The research approach is statistically descriptive by using data logs report on Learning Management System (LMS) Pusdiklat BMKG. The research object was 50 big data technical trainees from the technical implementation unit at BMKG, 13 teachers and 2 committees. The training was held at BMKG Education and Training Center from October 28 to December 9, 2019. The results showed that the behavior of trainees had significant activity only at certain moments. The LMS system records several events: 1) there is an increase in participant activity, when approaching the grace period of all tasks that is on Monday and during post-test implementation; 2) there is an increase in teacher activity when it is alerted by the committee to grading all the tasks collected by participants; 3) there is an increase in committee activity at the time the learning agenda begins and when the implementation of post-test.
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Feldman, Keith, and Nitesh V. Chawla. "Does Medical School Training Relate to Practice? Evidence from Big Data." Big Data 3, no. 2 (June 2015): 103–13. http://dx.doi.org/10.1089/big.2014.0060.
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Yu, Sheng, Yumeng Ma, Jessica Gronsbell, Tianrun Cai, Ashwin N. Ananthakrishnan, Vivian S. Gainer, Susanne E. Churchill, et al. "Enabling phenotypic big data with PheNorm." Journal of the American Medical Informatics Association 25, no. 1 (November 3, 2017): 54–60. http://dx.doi.org/10.1093/jamia/ocx111.
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Abstract Objective Electronic health record (EHR)-based phenotyping infers whether a patient has a disease based on the information in his or her EHR. A human-annotated training set with gold-standard disease status labels is usually required to build an algorithm for phenotyping based on a set of predictive features. The time intensiveness of annotation and feature curation severely limits the ability to achieve high-throughput phenotyping. While previous studies have successfully automated feature curation, annotation remains a major bottleneck. In this paper, we present PheNorm, a phenotyping algorithm that does not require expert-labeled samples for training. Methods The most predictive features, such as the number of International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes or mentions of the target phenotype, are normalized to resemble a normal mixture distribution with high area under the receiver operating curve (AUC) for prediction. The transformed features are then denoised and combined into a score for accurate disease classification. Results We validated the accuracy of PheNorm with 4 phenotypes: coronary artery disease, rheumatoid arthritis, Crohn’s disease, and ulcerative colitis. The AUCs of the PheNorm score reached 0.90, 0.94, 0.95, and 0.94 for the 4 phenotypes, respectively, which were comparable to the accuracy of supervised algorithms trained with sample sizes of 100–300, with no statistically significant difference. Conclusion The accuracy of the PheNorm algorithms is on par with algorithms trained with annotated samples. PheNorm fully automates the generation of accurate phenotyping algorithms and demonstrates the capacity for EHR-driven annotations to scale to the next level – phenotypic big data.
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Christozov, Dimitar, and Katia Rasheva-Yordanova. "Data Literacy." International Journal of Digital Literacy and Digital Competence 8, no. 2 (April 2017): 14–38. http://dx.doi.org/10.4018/ijdldc.2017040102.
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The article shares the authors' experiences in training bachelor-level students to explore Big Data applications in solving nowadays problems. The article discusses curriculum issues and pedagogical techniques connected to developing Big Data competencies. The following objectives are targeted: The importance and impact of making rational, data driven decisions in the Big Data era; Complexity of developing and exploring a Big Data Application in solving real life problems; Learning skills to adopt and explore emerging technologies; and Knowledge and skills to interpret and communicate results of data analysis via combining domain knowledge with system expertise. The curriculum covers: The two general uses of Big Data Analytics Applications, which are well distinguished from the point of view of end-user's objectives (presenting and visualizing data via aggregation and summarization [data warehousing: data cubes, dash boards, etc.] and learning from Data [data mining techniques]); Organization of Data Sources: distinction of Master Data from Operational Data, in particular; Extract-Transform-Load (ETL) process; and Informing vs. Misinforming, including the issue of over-trust vs. under-trust of obtained analytical results.
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Miao, Xia, and Yanping Wang. "Optimizing Language Teachers’ Competencies Based on Big Data Technology." Mathematical Problems in Engineering 2022 (July 30, 2022): 1–9. http://dx.doi.org/10.1155/2022/8935312.
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Using big data technology to promote the maturity and application of human analysis is the key to establish and maintain the competitive advantage of the school. Modern teachers must have the quality and ability to adapt to their work, that is, professional ability, in order to improve the teaching quality more pertinently. In order to effectively promote teachers’ professional growth, this paper proposes a Chinese teachers’ Ability Optimization Model Based on big data technology. Chinese teachers should take the initiative to meet the mission and challenges given by the times, strengthen Chinese teaching ability through the promotion of microability of information technology, explore the changes of learning and teaching style under the environment of information technology, and improve the ability of Chinese teachers. The research on the ability optimization of Chinese teachers based on big data technology in this paper can help schools optimize the training plan system by using the winning power model, that is, the training plan system can be improved from three aspects: determining the training objectives and contents and selecting the training methods. Teachers must improve their abilities through continuous learning, so as to promote education more actively and cultivate more talents for the country.
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Bares, Lee, Daniel Davis, Daniel Min, Kenneth Rau, and Matthew Dabkowski. "Developing a Solution to the TRADOC Analysis Center’s Big Data Problem: A Big Data Opportunity." Industrial and Systems Engineering Review 6, no. 2 (March 7, 2019): 82–87. http://dx.doi.org/10.37266/iser.2018v6i2.pp82-87.
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As data production, collection, and analytic techniques grow, emerging issues surrounding data management and storage challenge businesses and organizations around the globe. The US Army Training and Doctrine Command’s Analysis Center (TRAC) is no exception. For example, among TRAC's many tasks is the evaluation of new materiel solutions for the Army, which typically necessitates the use of computer simulation models such as COMBAT XXI. These models are computationally expensive, and they generate copious amounts of data, straining TRAC's current resources and forcing difficult, suboptimal decisions regarding data retention and analysis. This paper addresses this issue directly by developing "big data" solutions for TRAC and evaluating them using its organizational values. Framed in the context of a use case that prescribes system requirements, we leverage Monte Carlo simulation to account for inherent uncertainty and, ultimately, focus TRAC on several high potential alternatives.
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Huo, Yan, and Guangying Yang. "Study on talent educational reform model of new energy automation based on big data." E3S Web of Conferences 261 (2021): 01051. http://dx.doi.org/10.1051/e3sconf/202126101051.
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For the situation of energy depletion, the development of clean energy and powder has become a trend in the future. In order to explore the talent training for new energy automation, an innovative education model needs to be established to constitute an oriented training system from many aspects of teaching and learning based on big data. With modern information technology, the education hierarchical methods under the principle of society demand are used. The potential opportunities and challenges of new energy automation training is comprehensively analyzed, and the big data, feasible curriculum system and education modes are discussed. From the perspective of big data, the teaching strategies and teaching contents for the model based on big data are given respectively.
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I.V., Prokhorov, Kochetkov O.T., and Filatov A.A. "Practical Training of Students on the Extraction and Analysis of Big Data." KnE Social Sciences 3, no. 2 (February 15, 2018): 361. http://dx.doi.org/10.18502/kss.v3i2.1565.
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The article deals with questions of studies, development and practical use in teaching complex laboratory work on extracting and analyzing big data to train specialists in the specialty 10.05.04 "Information and Analytical Security Systems", direction of training "Information sSecurity of Financial and Economic Structures" in the framework of the educational discipline "Distributed Automated Information Systems". Keywords: big data, data scientist, extraction, processing and analysis of big data, information security of financial and economic structures, the Internet, Yandex, Google, application programming interface –API.
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Fritz, Roschelle L., and Gordana Dermody. "Interpreting Health Events in Big Data Using Qualitative Traditions." International Journal of Qualitative Methods 19 (January 1, 2020): 160940692097645. http://dx.doi.org/10.1177/1609406920976453.
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The training of artificial intelligence requires integrating real-world context and mathematical computations. To achieve efficacious smart health artificial intelligence, contextual clinical knowledge serving as ground truth is required. Qualitative methods are well-suited to lend consistent and valid ground truth. In this methods article, we illustrate the use of qualitative descriptive methods for providing ground truth when training an intelligent agent to detect Restless Leg Syndrome. We show how one interdisciplinary, inter-methodological research team used both sensor-based data and the participant’s description of their experience with an episode of Restless Leg Syndrome for training the intelligent agent. We make the case for clinicians with qualitative research expertise to be included at the design table to ensure optimal efficacy of smart health artificial intelligence and a positive end-user experience.
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Jiang, Z. H. "RESEARCH ON THE MAKER TEACHER MOBILE TRAINING PLATFORM BASED ON BIG DATA ANALYSIS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W10 (February 7, 2020): 343–48. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w10-343-2020.
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Abstract. In the era of "Internet +" and Big Data, it is of great practical significance on how to build a training platform that accurately matches the professional development of maker teachers, and to carry out personalized mobile training for maker teachers under the Big Data analysis technology. The construction of the maker teacher mobile training platform, based on the big data analysis technology, is designed to explore the personalized needs of maker teachers in professional development. It introduces a new concept of MOOC and community space design to build the maker mobile training platform framework structure, which contains three layers: application layer, service layer, and data layer. It designs five functional modules: diagnostic demand analysis module, personalized service customization module, online maker course module, seminar space module, and evaluation feedback module. The case analysis of the platform and its application effect shows that the maker teacher mobile training platform based on big data analysis has obvious effects on professional development for teachers and can provide reference for future research on related topics.
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Kim, You-Jin, and Kyu-Hoon Kwak. "Keyword Analysis of Home Training by Period Using Big Data." Korean Journal of Sports Science 30, no. 1 (February 28, 2021): 103–15. http://dx.doi.org/10.35159/kjss.2021.2.30.1.103.
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McEligot, Archana Jaiswal, Math P. Cuajungco, Sam Behseta, Laura Chandler, Harmanpreet Chauhan, Sinjini Mitra, Pimbucha Rusmevichientong, and Shana Charles. "Big Data Science Training Program at a Minority Serving Institution." Californian Journal of Health Promotion 16, no. 1 (June 1, 2018): 1–5. http://dx.doi.org/10.32398/cjhp.v16i1.2118.
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Hersh, W., A. U. Jai Ganesh, and P. Otero. "Big Data: Are Biomedical and Health Informatics Training Programs Ready?" Yearbook of Medical Informatics 23, no. 01 (August 2014): 177–81. http://dx.doi.org/10.15265/iy-2014-0007.
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Summary Objective: The growing volume and diversity of health and biomedical data indicate that the era of Big Data has arrived for healthcare. This has many implications for informatics, not only in terms of implementing and evaluating information systems, but also for the work and training of informatics researchers and professionals. This article addresses the question: What do biomedical and health informaticians working in analytics and Big Data need to know? Methods: We hypothesize a set of skills that we hope will be discussed among academic and other informaticians. Results: The set of skills includes: Programming - especially with data-oriented tools, such as SQL and statistical programming languages; Statistics - working knowledge to apply tools and techniques; Domain knowledge - depending on one’s area of work, bioscience or health care; and Communication - being able to understand needs of people and organizations, and articulate results back to them. Conclusion: Biomedical and health informatics educational programs must introduce concepts of analytics, Big Data, and the underlying skills to use and apply them into their curricula. The development of new coursework should focus on those who will become experts, with training aiming to provide skills in “deep analytical talent” as well as those who need knowledge to support such individuals.
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Haroon Ur Rashid, Fatma Hussain, and Khalid Masood. "Big Data and Precision Medicine." Lahore Garrison University Research Journal of Computer Science and Information Technology 2, no. 1 (March 30, 2018): 1–8. http://dx.doi.org/10.54692/lgurjcsit.2018.020140.
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This paper focuses on clinical data taken from diversified sources that can be utilized to predict medical conditions. Precision medicine being top priority in medication is main essence to describe treatment based on individual physiology, genetic makeup and other factors. Healthcare information is available from clinics, government hospitals and electronic medical records along with advanced digital resources such as glucometers, insulin injectors, blood pressure monitors, and smart watches. Social media is an excellent source where people share their medical treatment status on Facebook, Twitter, WhatsApp and LinkedIn. Effective statistical models can be created social media to prescribe medicines. Vital architectural components include storage programs (Amazon S3, Google cloud store), data incorporation mechanisms (Kafka, Storm Topology, Sqoop), APIs (Fitbit Web, Apple HealthKit, OneTouch, Facebook, Twitter), processing engine (Spark, Hadoop) and training datasets (Spark ML, Mahout scalable machine learning, data mining techniques, appropriate algorithms). Advantages of precision medicine includes powerful decision making resources (big data), better selection of disease targets, treatment opportunities, reduced medical expenses and timely delivery of healthcare. To optimize the capability of precision medicine, uninterrupted research funding, scientific initiatives, patient involvement in medicinal initiatives. Successful execution of precision medicine with holistic individually tailored approach necessitates the coordinated efforts of all healthcare stakeholders for its recognition, up-gradation of diagnosis and management.
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Li, Chunguang, and Jianbiao Cui. "Intelligent Sports Training System Based on Artificial Intelligence and Big Data." Mobile Information Systems 2021 (May 22, 2021): 1–11. http://dx.doi.org/10.1155/2021/9929650.
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All activities in training fields are for the improvement of athletes’ competitive abilities. A sports training system is an organizational system to achieve common goals. Competitive ability is one of the main manifestations of the evolution of the training system. With the rapid development of computer technology, people have begun to combine virtual reality and other technologies to achieve scientific sports-assisted training to eliminate traditional sports training that relied purely on experience. Pose estimation obtains the position, angle, and additional information about the human body in the image in a two-dimensional plane or three-dimensional space by establishing the mapping relationship between the human body features and the human body posture. This article demonstrates a golf-assisted training system to realize the transformation from an experience-based sports training method to a human motion analysis method, using artificial intelligence and big data. The swing posture parameters of the trainer and the coach are obtained using the posture estimation of a human body. Based on this information, an auxiliary training system is built. The two parameters of the joint angle trajectory and the posture similarity are used as auxiliary indicators to compare the trainers. The joint angle trajectory is analyzed, and the coach is guided based on the similarity of the posture.
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Hoang, Yen, Juliane Pfeil, Maja Zagorščak, Axel Y. A. Thieffry, Eftim Zdravevski, Živa Ramšak, Petre Lameski, et al. "Report on the “Advanced Big Data Training School for Life Sciences”, Barcelona 3th-7th September 2018." EMBnet.journal 24 (February 5, 2019): e917. http://dx.doi.org/10.14806/ej.24.0.917.
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The “Advanced Big Data Training School for Life Sciences” took place during September 3-7, 2018, organized by the Data Management Group (DAMA-UPC) at the Technical University of Catalonia (UPC) in Barcelona, Spain. It is the follow-up training school of the first “Big Data Training School for Life Sciences”, held in Uppsala, Sweden, in September 2017, which was defined and structured at the “Think Tank Hackathon”, held in Ljubljana, Slovenia, in February 2018. The aim of this training school was to get participants acquainted with emerging Big Data processing techniques in the field of Computational Biology and Bioinformatics.This article explains in detail the development of the training school, the covered contents and the interaction of the participants within and out of the training event by the student, organizer and lecturer perspective.
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Awan, Mazhar Javed, Umar Farooq, Hafiz Muhammad Aqeel Babar, Awais Yasin, Haitham Nobanee, Muzammil Hussain, Owais Hakeem, and Azlan Mohd Zain. "Real-Time DDoS Attack Detection System Using Big Data Approach." Sustainability 13, no. 19 (September 27, 2021): 10743. http://dx.doi.org/10.3390/su131910743.
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Currently, the Distributed Denial of Service (DDoS) attack has become rampant, and shows up in various shapes and patterns, therefore it is not easy to detect and solve with previous solutions. Classification algorithms have been used in many studies and have aimed to detect and solve the DDoS attack. DDoS attacks are performed easily by using the weaknesses of networks and by generating requests for services for software. Real-time detection of DDoS attacks is difficult to detect and mitigate, but this solution holds significant value as these attacks can cause big issues. This paper addresses the prediction of application layer DDoS attacks in real-time with different machine learning models. We applied the two machine learning approaches Random Forest (RF) and Multi-Layer Perceptron (MLP) through the Scikit ML library and big data framework Spark ML library for the detection of Denial of Service (DoS) attacks. In addition to the detection of DoS attacks, we optimized the performance of the models by minimizing the prediction time as compared with other existing approaches using big data framework (Spark ML). We achieved a mean accuracy of 99.5% of the models both with and without big data approaches. However, in training and testing time, the big data approach outperforms the non-big data approach due to that the Spark computations in memory are in a distributed manner. The minimum average training and testing time in minutes was 14.08 and 0.04, respectively. Using a big data tool (Apache Spark), the maximum intermediate training and testing time in minutes was 34.11 and 0.46, respectively, using a non-big data approach. We also achieved these results using the big data approach. We can detect an attack in real-time in few milliseconds.
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Dubarry, Matthieu, and David Beck. "Big data training data for artificial intelligence-based Li-ion diagnosis and prognosis." Journal of Power Sources 479 (December 2020): 228806. http://dx.doi.org/10.1016/j.jpowsour.2020.228806.
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Errezgouny, Abderrachid, and Abdeljabbar Cherkaoui. "Contribution in Big Data Projects Management." E3S Web of Conferences 351 (2022): 01066. http://dx.doi.org/10.1051/e3sconf/202235101066.
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Nowadays, the necessity of the data becomes more attractive by companies in different areas (IT, space, automotive) who need to create and capture the value from the huge amounts of data generated from various sources. Many fields need to use this amount in the right way in real time with high level processing, this evolution is called Big Data (BD). In this case, to manage a BD project the specific tools like Machine Learning, Data Mining, and more are very important to achieve the customer satisfaction with the expected quality of services. The majority of BD projects fall due to the lack of managing skills and team training, also the sophisticated materials and technologies are required. This paper presents our contribution in the project management of BD based on other discussed methods like Project Management Body of Knowledge (PMBoK) and Agile approaches, and we use them to construct a rigid model for managing any project dedicated to work with BD.
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Gan, Dufen. "Research on Higher Vocational Computer Education Based on Big Data Background." MATEC Web of Conferences 365 (2022): 01021. http://dx.doi.org/10.1051/matecconf/202236501021.
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With the application of big data technology becoming more and more mature, in the process of computer education in higher vocational colleges, we should adapt to the basic environment of the rapid development and application of intelligent technology, and stress the professional characteristics, it is necessary to work out a talent training plan that integrates big data and computer education in higher vocational colleges, reconstruct the computer education mode and teaching idea, and improve the training efficiency of computer talents in higher vocational colleges. Based on the big data environment, this paper puts forward the corresponding reform strategies in view of the practical problems existing in higher vocational computer education.
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Sun, Zhidong, and Xueqing Li. "Construction of Live Broadcast Training Platform Based on “Cloud Computing” and “Big Data” and “Wireless Communication Technology”." Wireless Communications and Mobile Computing 2021 (September 14, 2021): 1–9. http://dx.doi.org/10.1155/2021/8971195.
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With the rapid development of information technology, a scientific theory is brought by the rapid progress of science and technology. The advancement of science and technology of the impact on every field, changing the mode of transmission of information, the advent of big data for promotion and dissemination of resources played their part, let more and more people benefit. In the context of cloud computing, big data ushered in another upsurge of development and growth. Given this, the live broadcast training platform, which focuses on enterprise staff training and network education, arises at the right moment. People favor its convenience, real-time performance, and high efficiency. However, the low-value density of big data and cloud computing’s security problem has difficulties constructing a live broadcast training platform. In this paper, the live broadcast training platform’s structure is improved by constructing three modules: the live training module based on cloud computing, the user recommendation module based on big data, and the security policy guarantee module. In addition, to ensure that the trainees can receive training anytime and anywhere, this paper uses wireless communication technology to ensure the quality and speed of all users’ live video sources.
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Yang, Kenneth C. C., and Yowei Kang. "Big Data Analytics in Undergraduate Advertising Curricula." International Journal of Technology and Educational Marketing 8, no. 1 (January 2018): 34–47. http://dx.doi.org/10.4018/ijtem.2018010103.
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The rapid ascent of data-driven advertising practices has allowed advertising professionals to develop highly-targeted and personalized advertising campaigns. The success of data-driven advertising relies on if future professionals are proficient with basics of Big Data analytics. However, past research of undergraduate advertising curricula around the world has shown that higher education institutions tend to fall behind in offering the most up-to-dated training for advertising students. Findings have shown that undergraduate advertising programs have slowly taken advantage of the potential of the data analytics tools and techniques. This trend is observed among higher education institutions around the world. Practical, research, and pedagogical implications are discussed.
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Sedona, Rocco, Gabriele Cavallaro, Jenia Jitsev, Alexandre Strube, Morris Riedel, and Jón Benediktsson. "Remote Sensing Big Data Classification with High Performance Distributed Deep Learning." Remote Sensing 11, no. 24 (December 17, 2019): 3056. http://dx.doi.org/10.3390/rs11243056.
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High-Performance Computing (HPC) has recently been attracting more attention in remote sensing applications due to the challenges posed by the increased amount of open data that are produced daily by Earth Observation (EO) programs. The unique parallel computing environments and programming techniques that are integrated in HPC systems are able to solve large-scale problems such as the training of classification algorithms with large amounts of Remote Sensing (RS) data. This paper shows that the training of state-of-the-art deep Convolutional Neural Networks (CNNs) can be efficiently performed in distributed fashion using parallel implementation techniques on HPC machines containing a large number of Graphics Processing Units (GPUs). The experimental results confirm that distributed training can drastically reduce the amount of time needed to perform full training, resulting in near linear scaling without loss of test accuracy.
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Wang, Bin, Genutė Gedvilienė, Hongfeng Li, and XinYue Wang. "The Implementation of Network Big Data on Vocational College Teacher Training Strategy." Wireless Communications and Mobile Computing 2022 (June 30, 2022): 1–13. http://dx.doi.org/10.1155/2022/5485498.
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Teachers’ teaching level and teaching philosophy have an important impact on students. As the country pays more and more attention to education, the relevant level of teachers also needs to be continuously improved. Training for teachers is one of the important ways to improve teachers’ level. Although the strategy of training teachers in vocational colleges has a long history, there is no analysis of its implementation. With the popularity of the Internet of Things, lives are full of data information and data, and the field of education and training is no exception. Network big data refers to a collection of data that cannot be captured by conventional software tools within a certain time frame. It is an information asset that requires new processing modes to have stronger decision-making, insight, and process optimization capabilities. This paper aims to study the analysis of network big data on the implementation of teacher training policies in vocational colleges. It is expected that with the support of network big data, the implementation of vocational teachers’ training policies will be analyzed, and the implementation effects of relevant policies will be explored, so as to help teachers improve their professional abilities and promote the development of the education industry. In a broad sense, the implementation of educational strategy refers to the identification, construction, and termination of educational policies. Educational strategy in a narrow sense refers specifically to the educational strategy, educational setting plan, educational budget, and educational plan formulated by the competent educational authority. From the perspective of career management, this paper briefly analyzes the situation of teachers participating in training in vocational colleges and reexamines the connotation of current higher vocational teacher training. It takes the teacher training of vocational colleges as the content and makes a brief analysis of the relevant situations, attitudes, and achievements in the process of teacher training by means of a questionnaire survey. The results showed that the largest number of teachers who participated in the training received scores between 80 and 100 points, indicating that the implementation of training policies is in place; the proportion of schools that attach importance to teacher training is as high as 80%, indicating that the environment for strategy implementation is better.
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Brown, Sarah M., and Natasha T. Brison. "Big Data, Big Problems: Analysis of Professional Sports Leagues’ CBAs and Their Handling of Athlete Biometric Data." Journal of Legal Aspects of Sport 30, no. 1 (January 31, 2020): 63–81. http://dx.doi.org/10.18060/23894.
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The use and integration of wearable technology (wearables) into professional sports is increasing rapidly. At a minimum, the NFL, NBA, MLB, NHL, and MLS have all integrated wearables into their training. Teams’ hope the biometric data obtained from the wearables will sharpen athletic performance, create competitive advantages, enhance fan experience, and generate new revenue streams. However, to obtain these desired outcomes leagues must adequately protect their athlete’s biometric data (ABD). The purpose of this paper is to examine and compare the CBAs of the NFL, NBA, MLB, NHL, and MLS management of wearables and ABD. Specifically, this paper will discuss the potential gaps in protection of ABD within the CBA and explore whether federal and state laws are applicable to protect the data. Findings from this analysis improve our understanding of professional sport leagues management of ABD and expose the limitations of protection at the league, state, and federal level.
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Aikat, Jay, Thomas M. Carsey, Karamarie Fecho, Kevin Jeffay, Ashok Krishnamurthy, Peter J. Mucha, Arcot Rajasekar, and Stanley C. Ahalt. "Scientific Training in the Era of Big Data: A New Pedagogy for Graduate Education." Big Data 5, no. 1 (March 2017): 12–18. http://dx.doi.org/10.1089/big.2016.0014.
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Ruiz-Palmero, Julio, Ernesto Colomo-Magaña, José Manuel Ríos-Ariza, and Melchor Gómez-García. "Big Data in Education: Perception of Training Advisors on Its Use in the Educational System." Social Sciences 9, no. 4 (April 15, 2020): 53. http://dx.doi.org/10.3390/socsci9040053.
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Big Data has revolutionized decision making in many fields, including education. The incorporation of information and communication technologies into education enables us to gather information about the teaching and learning process. As Big Data can help us improve it, it is paramount to integrate it into initial and continuous learning stages. This study therefore aims at finding out the perception of the training advisors of teacher training centers (N = 117) in Andalusia on the application of Big Data in education. The tool is an adaptation of the VABIDAE (Assessment of Big Data Applied to Education) scale, and the study of the descriptive statistics was carried out by using the analysis of variance (ANOVA) and Mann–Whitney U tests in order to check the existence of significant differences and correlations between the items that make up the scale. The results reflect the positive perception of training advisors on the use of Big Data in education. Significant differences were found in the competence level variable, whereby this tool was better rated by those advisors who feel that they have an advanced competence level. In conclusion, Big Data is valued for its ability to personalize educational processes and the consequent improvement in academic results, which shows the need to increase the level of knowledge about this tool.