# A Survey on Video Big Data Analytics: Architecture, Technologies, and Open Research Challenges
## 4 ViMindXAI: A Scalable and Cognitive AI Platform for VBDA
### Comparative Analysis with Existing VBDA Platforms  
To contextualize the architectural contributions of ViMindXAI, we conducted a comparative analysis against representative VBDA platforms: FALKON [118], SIAT [119], SurveilEdge [52], and VIDEX [133]. These systems span centralized, cloud-based, edge-centric, and hybrid deployments and are widely cited in the literature. Our comparison focuses on architectural depth, semantic capability, explainability, and integration of emerging AI paradigms such as FL, XAI, and multimodal indexing. Table 9 summarizes this evaluation across ten core criteria, categorized by the functional layers of ViMindXAI (DAL, DIL, DaAL, UEL) and mapped to architectural types introduced in Section 3.  
In contrast to FALKON's centralized batch-based architecture or SurveilEdge's edge-focused object detection, ViMindXAI adopts a layered, hybrid cognitive framework. The DAL incorporates lightweight models (e.g., YOLOv8-tiny), preprocessing, and embedded FL at the edge, surpassing the ingestion-only approach in SIAT or the static logic in VIDEX. The DIL leverages an AI-optimized Lakehouse architecture, integrating orchestration tools (e.g., Delta Lake, Apache Iceberg), semantic indexing (e.g., CLIP, VideoBERT), and GraphDB-based reasoning, features absent in the compared platforms. In the DaAL, ViMindXAI integrates explainability (e.g., GradCAM, SHAP) with coordinated model management using TFF and NVIDIA FLARE, supporting privacy-preserving learning and accountability. The UEL enables immersive, adaptive dashboards and LLM-powered interfaces for semantic video interaction, extending far beyond the static dashboards found in prior systems.  
Although Table 9 offers a qualitative comparison, the architectural design of ViMindXAI is grounded in empirical evidence from recent federated VBDA systems. [PERSON] et al. [156] demonstrated that a hybrid VAE-SURF model, deployed using federated learning and edge computing, can achieve 25 ms latency and 40 fps throughput with only 2.5 GFLOPs per frame, highlighting its effectiveness for real-time anomaly detection on edge devices. Similarly, [PERSON] [157] introduced Dashlet, a cross-layer video streaming system that predicts user swipe behavior to optimize prefetching, achieving sub-100 ms latency improvements in live deployments.  
Drawing on these insights, ViMindXAI incorporates edge-side inference using CLIP, asynchronous federated learning via FedAvg and FedProx, and orchestration through KubeEdge and TFF. These design choices enable real-time semantic search, scalable explainability, and low-latency responsiveness while maintaining practical deployability across diverse infrastructures. Although full-scale benchmarking is planned as part of future work in Section 6, the current architecture reflects a well-founded integration of proven strategies tailored to the performance and privacy demands of modern VBDA.  
While ViMindXAI is in early-stage prototyping, all integrated components are mature, open source, and production-ready. The platform is designed for heterogeneous deployment environments via an edge-fog-cloud topology: edge nodes perform inference on devices like Jetson or Coral TPU; fog nodes handle caching and policy enforcement; the cloud layer oversees training and coordination. It supports microservice and master-slaver orchestration patterns, with federated agents managing updates under privacy and bandwidth constraints. Network features like 5G/6G, SDN, and QoS are embedded in task scheduling and data routing, ensuring readiness for real-world, large-scale deployments.