A Survey on Video Big Data Analytics: Architecture, Technologies, and Open Research Challenges
The exponential growth of video data across domains such as surveillance, transportation, and healthcare has raised critical challenges in scalability, real-time processing, and privacy preservation. While existing studies have addressed individual aspects of Video Big Data Analytics (VBDA), an inte...
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MDPI AG
2025-07-01
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| author | Thi-Thu-Trang Do Quyet-Thang Huynh Kyungbaek Kim Van-Quyet Nguyen |
| author_facet | Thi-Thu-Trang Do Quyet-Thang Huynh Kyungbaek Kim Van-Quyet Nguyen |
| author_sort | Thi-Thu-Trang Do |
| collection | DOAJ |
| description | The exponential growth of video data across domains such as surveillance, transportation, and healthcare has raised critical challenges in scalability, real-time processing, and privacy preservation. While existing studies have addressed individual aspects of Video Big Data Analytics (VBDA), an integrated, up-to-date perspective remains limited. This paper presents a comprehensive survey of system architectures and enabling technologies in VBDA. It categorizes system architectures into four primary types as follows: centralized, cloud-based infrastructures, edge computing, and hybrid cloud–edge. It also analyzes key enabling technologies, including real-time streaming, scalable distributed processing, intelligent AI models, and advanced storage for managing large-scale multimodal video data. In addition, the study provides a functional taxonomy of core video processing tasks, including object detection, anomaly recognition, and semantic retrieval, and maps these tasks to real-world applications. Based on the survey findings, the paper proposes ViMindXAI, a hybrid AI-driven platform that combines edge and cloud orchestration, adaptive storage, and privacy-aware learning to support scalable and trustworthy video analytics. Our analysis in this survey highlights emerging trends such as the shift toward hybrid cloud–edge architectures, the growing importance of explainable AI and federated learning, and the urgent need for secure and efficient video data management. These findings highlight key directions for designing next-generation VBDA platforms that enhance real-time, data-driven decision-making in domains such as public safety, transportation, and healthcare. These platforms facilitate timely insights, rapid response, and regulatory alignment through scalable and explainable analytics. This work provides a robust conceptual foundation for future research on adaptive and efficient decision-support systems in video-intensive environments. |
| format | Article |
| id | doaj-art-dbc4d8b900d14e048df708464fd8ef41 |
| institution | DOAJ |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-07-01 |
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| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-dbc4d8b900d14e048df708464fd8ef412025-08-20T03:13:42ZengMDPI AGApplied Sciences2076-34172025-07-011514808910.3390/app15148089A Survey on Video Big Data Analytics: Architecture, Technologies, and Open Research ChallengesThi-Thu-Trang Do0Quyet-Thang Huynh1Kyungbaek Kim2Van-Quyet Nguyen3School of Information and Communication Technology, Hanoi University of Science and Technology, Hanoi 100000, VietnamSchool of Information and Communication Technology, Hanoi University of Science and Technology, Hanoi 100000, VietnamDepartment of Artificial Intelligence Convergence, Chonnam National University, Gwangju 61186, Republic of KoreaFaculty of Information Technology, Hung Yen University of Technology and Education, Hung Yen 160000, VietnamThe exponential growth of video data across domains such as surveillance, transportation, and healthcare has raised critical challenges in scalability, real-time processing, and privacy preservation. While existing studies have addressed individual aspects of Video Big Data Analytics (VBDA), an integrated, up-to-date perspective remains limited. This paper presents a comprehensive survey of system architectures and enabling technologies in VBDA. It categorizes system architectures into four primary types as follows: centralized, cloud-based infrastructures, edge computing, and hybrid cloud–edge. It also analyzes key enabling technologies, including real-time streaming, scalable distributed processing, intelligent AI models, and advanced storage for managing large-scale multimodal video data. In addition, the study provides a functional taxonomy of core video processing tasks, including object detection, anomaly recognition, and semantic retrieval, and maps these tasks to real-world applications. Based on the survey findings, the paper proposes ViMindXAI, a hybrid AI-driven platform that combines edge and cloud orchestration, adaptive storage, and privacy-aware learning to support scalable and trustworthy video analytics. Our analysis in this survey highlights emerging trends such as the shift toward hybrid cloud–edge architectures, the growing importance of explainable AI and federated learning, and the urgent need for secure and efficient video data management. These findings highlight key directions for designing next-generation VBDA platforms that enhance real-time, data-driven decision-making in domains such as public safety, transportation, and healthcare. These platforms facilitate timely insights, rapid response, and regulatory alignment through scalable and explainable analytics. This work provides a robust conceptual foundation for future research on adaptive and efficient decision-support systems in video-intensive environments.https://www.mdpi.com/2076-3417/15/14/8089video big data analyticsedge AI and federated learningreal-time video processinghybrid cloud–edge computingprivacy-preserving AI |
| spellingShingle | Thi-Thu-Trang Do Quyet-Thang Huynh Kyungbaek Kim Van-Quyet Nguyen A Survey on Video Big Data Analytics: Architecture, Technologies, and Open Research Challenges Applied Sciences video big data analytics edge AI and federated learning real-time video processing hybrid cloud–edge computing privacy-preserving AI |
| title | A Survey on Video Big Data Analytics: Architecture, Technologies, and Open Research Challenges |
| title_full | A Survey on Video Big Data Analytics: Architecture, Technologies, and Open Research Challenges |
| title_fullStr | A Survey on Video Big Data Analytics: Architecture, Technologies, and Open Research Challenges |
| title_full_unstemmed | A Survey on Video Big Data Analytics: Architecture, Technologies, and Open Research Challenges |
| title_short | A Survey on Video Big Data Analytics: Architecture, Technologies, and Open Research Challenges |
| title_sort | survey on video big data analytics architecture technologies and open research challenges |
| topic | video big data analytics edge AI and federated learning real-time video processing hybrid cloud–edge computing privacy-preserving AI |
| url | https://www.mdpi.com/2076-3417/15/14/8089 |
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