# A Survey on Video Big Data Analytics: Architecture, Technologies, and Open Research Challenges
## 6 Conclusions  
This paper presented a comprehensive survey of the evolution of VBDA, highlighting the shift from centralized pipelines to hybrid cloud-edge platforms enhanced by deep learning, federated learning, and explainable AI. We identified key challenges, such as latency, scalability, data heterogeneity, and privacy, and introduced ViMindXAI, a modular platform designed to address these limitations through emerging AI and infrastructure techniques. The layered design of the platform aligns closely with the architectural gaps outlined in this survey, offering a coherent response to system-level and user-centric demands in modern video analytics. By mapping critical challenges to targeted solutions, ViMindXAI contributes a unified framework for building scalable, intelligent, and privacy-aware video analytics systems. These findings point to key directions for developing next-generation VBDA platforms that support real-time, data-driven decision-making in domains such as public safety, transportation, and healthcare. They also provide a solid foundation for future research on adaptive and efficient decision-support systems in video-intensive environments.  
In the future, to advance ViMindXAI from conceptual design to real-world deployment, we outline a phased research roadmap. In the short term, we first focus on improving real-time scalability and privacy-preserving learning through reinforcement learning-based scheduling, predictive task offloading, and self-supervised training. As an initial step, ViMindXAI will be prototyped in a smart building surveillance scenario to evaluate latency, accuracy under non-IID data, privacy, and responsiveness. We then plan to prioritize lightweight model optimization for edge devices, secure federated learning with differential privacy, and blockchain-based metadata governance. In the long term, our efforts will explore quantum-inspired models and self-supervised techniques for semantic video retrieval. Empirical validation will be conducted in real-world domains such as healthcare monitoring and industrial safety, which demand low-latency processing, robust privacy protection, and high model transparency.  
Conceptualization, T.-T.-T.D. and V.-Q.N.; methodology, formal analysis, and writing: original draft preparation, T.-T.-T.D.; resources, visualization, writing: review and editing, V.-Q.N., Q.-T.H. and K.K.; supervision, project administration, and funding acquisition, V.-Q.N. and K.K. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Hung Yen University of Technology and Education under the grant number UTEHY.L.2023.02. This work was supported by the Institute of Information & Communications Technology Planning & Evaluation (IITP)-Innovative Human Resource Development for Local Intellectualization program grant funded by the Korea government (MSIT) (IITP-2025-RS-2022-00156287).  
Not applicable.  
The data that support the findings of this study are available from the corresponding author upon reasonable request.  
The authors declare no conflict of interest.