# A Survey on Video Big Data Analytics: Architecture, Technologies, and Open Research Challenges
## 2 Literature Overview in VBDA
### Evolution of Architectural Paradigms in VBDA  
Over the past decade, VBDA architectures have progressively transitioned from rigid centralized pipelines to distributed and collaborative models. Traditional systems were monolithic and tightly coupled to specific applications, typically running on standalone servers and using tools such as FFmpeg or GStreamerfor basic media processing [24; 34; 35]. Although suitable for offline tasks, these early setups presented significant limitations in scalability, modularity, and integration with big data ecosystems, rendering them ineffective for high-throughput or real-time video analytics. This evolution phase was marked by the introduction of distributed batch processing frameworks, including Apache Hadoop and Apache Spark, which enabled greater parallelism and scalability [36; 37]. These architectures enabled large-scale video indexing and archive-based analysis, proving effective for retrospective tasks. However, their batch-oriented nature came with high latency and reliance on centralized coordination, making them unsuitable for time-sensitive domains like smart surveillance or autonomous systems. To support real-time inference and more dynamic workloads, cloud-centric architectures emerged as a flexible and scalable alternative. These systems utilized elastic cloud resources for deep learning tasks such as object detection, summarization, and large-scale storage [11; 28]. Despite offering scalability, centralized cloud designs introduce latency, bandwidth overhead, and regulatory concerns associated with transmitting raw video data [38], thereby necessitating decentralized alternatives. Edge computing addressed these limitations by pushing inference and preprocessing closer to data sources, including IP cameras and sensors. This approach reduced network load and latency, enabling faster decision-making in critical applications like autonomous driving and real-time surveillance [4, 5]. However, edge systems continue to encounter challenges related to hardware heterogeneity, constrained computational resources, and synchronization of models across distributed nodes [3, 14]. In response, hybrid cloud-edge architectures have become the dominant model in modern VBDA. These systems offload real-time tasks to edge devices while leveraging the cloud for orchestration, storage, and training [4, 20]. Hybrid architectures offer a balanced trade-off between latency, scalability, and data privacy, yet they introduce orchestration complexity and unresolved challenges in distributed indexing, workload balancing, and coordination [5]. Hybrid architectures are increasingly evolving to incorporate generative AI and context-aware orchestration mechanisms. Recent paradigms within the edge-to-cloud spectrum are designed to enable semantic-level video understanding through capabilities such as real-time summarization, anomaly detection, and autonomous analytics [31]. This architectural continuum facilitates seamless coordination between resource-constrained edge devices and scalable cloud infrastructures [39], thereby enabling distributed intelligence across heterogeneous computing layers. Nevertheless, the intensive computational requirements of generative and multimodal AI models introduce new challenges. These include the need for advanced task scheduling, intelligent compression techniques, and resilient synchronization frameworks to ensure efficient operation across diverse environments. In summary, the evolution of VBDA architectures reflects ongoing efforts to balance scalability, latency, and privacy. Early monolithic and batch-based systems lacked responsiveness and flexibility. Cloud-based models improved scalability but raised concerns over latency and data privacy. Edge architectures reduced delay and preserved data locality but faced resource and coordination limits. Hybrid designs, integrating edge and cloud, describe a balanced approach with adaptive workload distribution and semantic analytics.