# A Survey on Video Big Data Analytics: Architecture, Technologies, and Open Research Challenges
## 1 Introduction  
The explosive growth of video data has transformed multiple sectors, including transportation, healthcare, surveillance, and retail. According to Statista [1], video accounted for over 80% of global internet traffic in 2023, largely driven by the increase of smart cameras, IoT devices, autonomous systems, and online video platforms. This growth demandsscalable, real-time, and privacy-preserving Video Big Data Analytics (VBDA) systems to support intelligent decision-making across distributed environments. VBDA has become an integral part of modern urban infrastructure, as seen in smart city projects such as Singapore's traffic monitoring and electronic road pricing (ERP) systems, helping to reduce congestion and optimize signal flow [2]. In the healthcare domain, AI-driven video surveillance supports early detection of critical events in hospital environments, contributing to improved clinical outcomes [3]. In the retail sector, systems like Amazon Go leverage VBDA to monitor customer behavior, automate checkouts, and deliver personalized services [4]. Despite the promising real-world applications of VBDA, its development remains challenged by the high volume, heterogeneity, and complexity of video data. Video streams are inherently multimodal and high-dimensional, encompassing spatial, temporal, and auditory characteristics, and are often encoded in varied formats such as MP4, AVI, and MKV [5]. These characteristics introduce difficulties in decoding, indexing, and storage management [6; 7]. Real-time video analytics faces latency, bandwidth, and storage limitations, particularly in safety-critical applications such as autonomous driving and emergency response [4; 8]. Furthermore, privacy and data protection regulations such as GDPR and HIPAA require the incorporation of secure and explainable AI methodologies [3; 9]. Although distributed data processing frameworks such as Apache Spark, Kafka, and Flink [10; 11] have proven to be effective for scalability, they often fail to deliver low-latency responses and video-specific optimizations. Recent advances in edge AI and Federated Learning (FL) have led to a shift toward decentralized analytics closer to the data source [5; 12; 13]. FL, in particular, enables collaborative training between edge devices without transmitting raw data, which in turn improves privacy and reduces the load on the network [9; 14]. However, challenges such as model synchronization, non-independent and identically distributed (non-IID) data distributions, and edge-device constraints remain open research issues [15; 16]. This paper presents a comprehensive synthesis of the current landscape in VBDA, focusing on system architectures, enabling technologies, and application-oriented scenarios. Existing VBDA platforms are systematically categorized into our representative system architectures as follows: centralized, cloud-based, edge computing, and hybrid cloud-edge models. Concurrently, the study analyzes a broad spectrum of enabling technologies, including real-time stream processing frameworks, scalable distributed processing engines, deep learning, and federated learning models for intelligent analytics, and advanced storage solutions for managing multimodal, high-volume video data. Beyond architectural and technological classification, the study highlights critical challenges that persist throughout the VBDA analytical process, particularly regarding latency constraints, storage efficiency, and privacy preservation in distributed learning settings. To address these challenges, this paper proposes ViMindXAI, a hybrid AI-driven platform that incorporates edge and cloud orchestration, adaptive video storage mechanisms, privacy-aware learning processes, and intelligent semantic-level video retrieval capabilities. Our work, as described in this paper, makes three main contributions.