Research on the Application of Computer Science and Technology in the Context of Big Data
Keywords:
Big data, Computer science, Technology applicationAbstract
This article explores cutting-edge research on the application of computer science and technology in the context of big data. By deeply analyzing data collection, cleaning, preprocessing techniques, as well as efficient storage and management strategies, it reveals the core technologies and methods for big data processing. It discusses algorithm optimization and machine learning applications in big data analysis, emphasizing the important role of computer science and technology in mining data value and enhancing decision-making efficiency. It also focuses on big data security and privacy protection issues, proposing corresponding technical solutions. This article aims to provide reference and guidance for research on computer science and technology in the era of big data.
References
Tang, Z., Feng, Y., Zhang, J., & Wang, Z. (2026). SVD-BDRL: A trustworthy autonomous driving decision framework based on sparse voxels and blockchain enhancement. Alexandria Engineering Journal, 134, 433-446.
Lu, K., Sui, Q., Chen, X., & Wang, Z. (2025). NeuroDiff3D: a 3D generation method optimizing viewpoint consistency through diffusion modeling. Scientific Reports, 15(1), 41084.
Q. Tian, D. Zou, Y. Han and X. Li, "A Business Intelligence Innovative Approach to Ad Recall: Cross-Attention Multi-Task Learning for Digital Advertising," 2025 IEEE 6th International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT), Shenzhen, China, 2025, pp. 1249-1253, doi: 10.1109/AINIT65432.2025.11035473.
Deng, X., & Yang, J. (2025, August). Multi-Layer Defense Strategies and Privacy Preserving Enhancements for Membership Reasoning Attacks in a Federated Learning Framework. In 2025 5th International Conference on Computer Science and Blockchain (CCSB) (pp. 278-282). IEEE.
Sultan, N., Patwar, N., Wei, X., Chew, J., Liu, J., & Du, R. (2026). FedGuard: A Robust Federated AI Framework for Privacy-Conscious Collaborative AML, Inspired by DARPA GARD Principles. International Academic Journal of Social Science, 2, 1–16. https://doi.org/10.5281/zenodo.18253151
Hu, Xiao. "Low-Cost 3D Authoring via Guided Diffusion in GUI-Driven Pipeline." (2025).
Tan, C., Gao, F., Song, C., Xu, M., Li, Y., & Ma, H. (2024). Highly Reliable CI-JSO based Densely Connected Convolutional Networks Using Transfer Learning for Fault Diagnosis.
Zhang, X. (2024). Research on Dynamic Adaptation of Supply and Demand of Power Emergency Materials based on Cohesive Hierarchical Clustering. Innovation & Technology Advances, 2(2), 59–75. https://doi.org/10.61187/ita.v2i2.135
Zhou, J., & Cen, W. (2024). Investigating the Effect of ChatGPT-like New Generation AI Technology on User Entrepreneurial Activities. Innovation & Technology Advances, 2(2), 1–20. https://doi.org/10.61187/ita.v2i2.124
Yuan, Y., & Xue, H. (2025). Cross-Media Data Fusion and Intelligent Analytics Framework for Comprehensive Information Extraction and Value Mining.
Yuan Y, Xue H. Multimodal Information Integration and Retrieval Framework Based on Graph Neural Networks[C]//Proceedings of the 2025 4th International Conference on Big Data, Information and Computer Network. 2025: 135-139.
Li Z, Ji Q, Ling X, et al. A comprehensive review of multi-agent reinforcement learning in video games[J]. Authorea Preprints, 2025.
Zhang, Z., Wang, J., Li, Z., Wang, Y., & Zheng, J. (2025). Anncoder: A mti-agent-based code generation and optimization model. Symmetry, 17(7), 1087.
Wang, Q., Li, Y., & Myers, B. A. (2023). Designing AI-powered interactive systems with large language models. Communications of the ACM, 66(9), 78–87.
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Copyright (c) 2026 Junfang Wang
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
