# A Survey on Video Big Data Analytics: Architecture, Technologies, and Open Research Challenges
## 5 Open Research Challenges and Future Directions
### Integration of Emerging Technologies  
Breakthroughs in blockchain, quantum computing, SSL, and generative AI are reshaping the evolving VBDA landscape. These technologies hold the potential to redefine system capabilities if integrated thoughtfully.  
**Blockchain for Secure Access Control and Metadata Logging:** Blockchain can offer immutable logging, decentralized identity management, and transparent access auditing for video streams [144]. Smart contracts can enforce granular access policies, yet the latency and storage demands of public blockchains limit their use in real-time systems. Research on hybrid blockchain-cloud architectures could balance performance with data integrity and transparency.  
**Quantum Computing for Video Analytics:** Quantum-enhanced algorithms hold promise for accelerating complex video processing tasks, including encryption, compression, and content-based search. However, practical limitations such as decoherence, error rates, and hardware immaturity still hinder adoption. Future directions should explore hybrid quantum-classical pipelines and quantum-friendly video representations.  
**Self-Supervised Learning and Vision Transformers:** Annotation remains a major bottleneck in training video models. SSL methods such as masked video modeling, contrastive learning, and temporal prediction can learn meaningful representations from unlabeled data [7]. Coupled with ViTs, these approaches support scalable and label-efficient learning pipelines, with strong performance across retrieval, summarization, and activity recognition.  
**Generative AI for Video Understanding:** Generative models like GANs and diffusion architectures enable applications such as scene completion, synthetic data generation, and anomaly simulation [144]. While these tools enhance model robustness, they raise concerns around deepfake misuse, high training costs, and instability. Future research should prioritize training efficiency, transparent evaluation benchmarks, and the development of safeguards for ethical deployment.  
To support a pragmatic transition from conceptual design to real-world deployment, we propose a phased research roadmap. In the short term, our development efforts will focus on investigating real-time scalability and privacy-preserving learning. Specifically, we intend to incorporate reinforcement learning-based scheduling, predictive task offloading, and self-supervised learning techniques to enable label-efficient model training under constrained cloud-edge environments. As an initial step toward implementation, we plan to prototype ViMindXAI through a smart building surveillance and access management scenario, where multiple edge cameras monitor common areas to detect anomalous events such as unauthorized access or falls (as presented in Case Study 1, Section 4.3). The system will preserve user privacy through federated learning, supported by ONNX-compatible lightweight models and adaptive task schedulers. This experimental deployment will serve to evaluate latency, inference accuracy under non-IID data distributions, privacy guarantees, and system responsiveness in resource-constrained edge settings. In the medium term, we aim to enhance the platform's trustworthiness and accountability by integrating blockchain-based access control mechanisms and fairness auditing pipelines. In the long term, we will explore the potential of emerging paradigms such as quantum computing and generative AI, contingent upon advances in infrastructure maturity and algorithmic stability.