# A Survey on Video Big Data Analytics: Architecture, Technologies, and Open Research Challenges
## 4 ViMindXAI: A Scalable and Cognitive AI Platform for VBDA
### Real-World Application Scenarios of ViMindXAI
#### 4.3.1 Case Study 1: Smart Building Surveillance and Access Management  
ViMindXAI addresses the increasing demand for intelligent and privacy-aware surveillance in smart building environments. As IoT-enabled camera networks continue to expand,  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Components** & **Applicable Technologies** \\ \hline Edge Inference & YOLOv8-tiny, MobileNet, TensorRT, TFLite, OpenVINO, NVIDIA Jetson \\ FL Frameworks & TensorFlow Federated, NVIDIA FLARE, FedAvg, Secure Multi-Party \\ Stream Processing & Apache Kafka, Apache Flink, Spark Streaming, Apache NiFi \\ Hybrid and AI-Optimized & Hadoop HDFS, MinIO, AWS S3, Ceph, Redis \\ Storage & \\ Structured and Semantic Storage & Delta Lake, PostgreSQL, MongoDB, HBase, GraphDB \\ Search and Indexing & FAISS, OpenAI CLIP, Pinceone, Whisper, Elasticsearch, VideoBERT \\ Privacy and Security & Homomorphic Encryption, Differential Privacy, Zero-Knowledge Proofs (ZKYR), RBAC, Intel SGX \\ UI/UX and Visualization & Tableau, Kibana, Grafana, Power BI, Unity, Meta Quest, GPT, HoloLens, GradCAM, SHAP \\ \hline \hline \end{tabular}
\end{table}
Table 8: Core technologies used across ViMindXAI layers.  
the resulting surge in real-time video streams necessitates analytics systems that are both scalable and compliant with privacy regulations. ViMindXAI adopts a modular edge-cloud architecture designed to support distributed threat detection, anomaly analysis, and semantic video retrieval across heterogeneous sources.  
At the DAL, video streams are collected from IP cameras and processed locally on edge devices such as NVIDIA Jetson. Lightweight models like YOLOv8-tiny are used at this layer for early-stage filtering and entity detection, helping reduce transmission load. Preprocessing tasks, including frame selection and compression, further optimize bandwidth usage. Robust ingestion pipelines built on Apache Kafka and NiFi ensure reliable, fault-tolerant data flow to downstream components.  
In the DIL, hybrid storage is orchestrated using MinIO and Delta Lake, organizing video data across logical zones to manage lifecycle stages. Semantic indexing is powered by CLIP and Whisper embeddings, stored in FAISS to support fast, vector-based retrieval. Federated synchronization strategies enable local storage autonomy while contributing to a shared global index under privacy-preserving constraints.  
The DaAL executes core analytics workflows such as anomaly detection and person re-identification using advanced inference models. YOLOv11 is deployed here for high-precision object detection tasks in centralized pipelines, while Transformer and GNN architectures are employed for complex spatiotemporal reasoning. These models are updated through FL strategies like FedAvg. Explainable AI (XAI) modules provide transparent and interpretable outputs to support operational decisions.  
Finally, the UEL delivers interactive dashboards via Grafana, enabling semantic search queries (e.g., \"loitering near exit C\"), spatial heatmaps, and real-time alerting. These tools support monitoring, incident response, and forensic analysis while enhancing trust through visual explanations.  
By combining semantic intelligence, scalable infrastructure, and privacy-centric coordination, ViMindXAI delivers a practical and regulation-compliant platform for smart surveillance. Its architecture incorporates the most adopted technologies in modern VBDA systems, reinforcing its applicability in real-world deployments.