Editorial: Big data technologies and applications

Wu, Yulei; Pan, Yi; Barnaghi, Payam; Tan, Zhiyuan; Ge, Jingguo; Wang, Hao

doi:10.1007/s11276-021-02787-9

Cited by 3 publications

(2 citation statements)

References 23 publications

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“…In order to further demonstrate the actual effectiveness of China's financial industry information security platform based on computer big data, a comparative analysis will be conducted on the differences between China's financial industry information security platform based on computer big data and traditional security technology platforms around privacy leakage risk [8]. The experiment consists of three servers, namely the main server, server 2, and server 3.…”

Section: Experiments and Results Analysismentioning

confidence: 99%

Construction of Information Security Platform for China's Financial Industry Based on Big Data

Zhang

2024

Proceedings of the 5th Management Science Informatization and Economic Innovation Development Conference, MSIEID 2023, December

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In order to understand the construction of China's financial industry information security platform, a research on the construction of China's financial industry information security platform based on big data was put forward. In this paper, the financial information security in the era of big data is comprehensively described, and the main risks and challenges faced by financial information security in the emerging stage are analyzed. Secondly, in view of the hidden dangers of financial information security in the era of big data, this paper puts forward corresponding measures to enhance the security capability of financial information security in the era of big data and effectively promote the stable economic and social development.

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Section: Experiments and Results Analysismentioning

confidence: 99%

Construction of Information Security Platform for China's Financial Industry Based on Big Data

Zhang

2024

Proceedings of the 5th Management Science Informatization and Economic Innovation Development Conference, MSIEID 2023, December

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“…Therefore, in vocal music education, the function of big data video is brought into play. Through the function of big data video, students are vividly shown the singing skills on the stage, and then the key points are comprehensively explained to students, so that students realize the necessity of stage manners in vocal music performance, and aesthetic education is subtly carried out in this process [8]. After that, combined with the big data video recording function, the whole process of students' performances was recorded, and the students' vocal music manners were objectively evaluated and commented with the screen.…”

Section: Significantly Improve Teaching Efficiency 3 Big Data Technol...mentioning

confidence: 99%

Application of Big Data Technology in Vocal Music Teaching

2024

Proceedings of the 3rd International Conference on Internet Technology and Educational Informatization, ITEI 2023, November 24–

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In order to understand the application of big data technology in vocal music teaching, an application research based on big data technology in vocal music teaching was put forward. Firstly, this paper analyzes that the application of big data technology in vocal music teaching has far-reaching significance. In addition to effectively reforming educational means, it can also significantly improve classroom teaching efficiency, mobilize students' product polarity in vocal music learning, and significantly improve teachers' professional education skills and students' vocal music level. Secondly, it analyzes the current situation and problems of music teaching at this stage, and then tells the application significance of big data technology in vocal music teaching. Finally, it discusses the application strategy of data technology in vocal music teaching, which lays a sufficient theoretical foundation for the development of vocal music education industry and promotes the development and reform of vocal music education industry.

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Optimization of parallel SVM algorithm for big data

Xue,

Cai

2024

JCM

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Parallel Support Vector Machine (SVM) based on big data has achieved some results in data mining, but due to the complexity of the data itself and a large amount of noisy data, its execution efficiency and classification accuracy in the big data environment are very low. In order to eliminate noise, a noise reduction method based on Noise Cleaning (NC) strategy was proposed, and redundant training samples in big data environments were deleted; Introduce an improved Artificial Fish Swarm Algorithm (IAFSA) to obtain the final Parallel SVM algorithm using mutual information and artificial fish swarm algorithm based on MapReduce (MIAFSA-PSVM) classification model. The results indicate that when compared to CMI-PSVM, the execution time of MIAFSA-PSVM algorithm is higher on the NDC dataset with the largest data size, The SVM parameter optimization algorithm based on MapReduce and cuckoo search (CSSVM-MR) and the particle swarm optimization based parallel support vector machine ensemble algorithm (PSO-PSVM) decreased by 40.1%, 79.3%, and 51.7%, respectively. This indicates that GIESVM-MR and MIAFSA-PSVM have strong adaptability to big data environments and high classification accuracy.

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Editorial: Big data technologies and applications

Abstract: Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Cited by 3 publications

References 23 publications

Construction of Information Security Platform for China's Financial Industry Based on Big Data

Construction of Information Security Platform for China's Financial Industry Based on Big Data

Application of Big Data Technology in Vocal Music Teaching

Optimization of parallel SVM algorithm for big data

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