The exponential growth of video data across domains such as surveillance, transportation, and healthcare has raised critical challenges in scalability, real-time processing, and privacy preservation. While existing studies have addressed individual aspects of Video Big Data Analytics (VBDA), an integrated, up-to-date perspective remains limited. This paper presents a comprehensive survey of system architectures and enabling technologies in VBDA. It categorizes system architectures into four primary types as follows: centralized, cloud-based infrastructures, edge computing, and hybrid cloud–edge. It also analyzes key enabling technologies, including real-time streaming, scalable distributed processing, intelligent AI models, and advanced storage for managing large-scale multimodal video data. In addition, the study provides a functional taxonomy of core video processing tasks, including object detection, anomaly recognition, and semantic retrieval, and maps these tasks to real-world applications. Based on the survey findings, the paper proposes ViMindXAI, a hybrid AI-driven platform that combines edge and cloud orchestration, adaptive storage, and privacy-aware learning to support scalable and trustworthy video analytics. Our analysis in this survey highlights emerging trends such as the shift toward hybrid cloud–edge architectures, the growing importance of explainable AI and federated learning, and the urgent need for secure and efficient video data management. These findings highlight key directions for designing next-generation VBDA platforms that enhance real-time, data-driven decision-making in domains such as public safety, transportation, and healthcare. These platforms facilitate timely insights, rapid response, and regulatory alignment through scalable and explainable analytics. This work provides a robust conceptual foundation for future research on adaptive and efficient decision-support systems in video-intensive environments.

In large buildings, during the situation of fire or other hazards, a smart evacuation guidance system needs to fully consider manifold aspects of hazards to guide evacuees through exit gates as fast as possible by dynamic and safe routes. In this paper, we propose a smart evacuation guidance system with a dynamic evacuation routing approach by using the LCDT (Length-Capacity-Density-Trustiness) weighted graph model and partial view (PV) information which represents the hazard intensity and the crowd congestion information of a group of sections/floors in the building. The proposed system is designed as a distributed system with multiple layers of computing by using smart indicators. Given such a system, we develop an efficient distributed approach, so-called LCDT&PV, to find out effective evacuation routes dynamically. We then propose an estimating congestion strategy in order to improve the efficiency of dynamic evacuation routes. To validate the proposed system, we implement a simulator to compare the proposed evacuation routing approach with baseline approaches. Experimental results show that the proposed approach reduces up to 30% of the total evacuation time compared with others. Furthermore, through the results of initial smart indicator implementation, which can interact with the simulator, we show the viability of the proposed system.

A graph database is a type of NoSQL database that uses graph structure for semantic queries with nodes, edges, and properties to represent and store data. It has been applied in many fields, such as education, health, business, and social network, with many famous applications such as Google, Facebook, and eBay. One of the main advantages of the graph database is its effective performance in data queries. This paper presents a comprehensive comparison of the performance based on the execution time of a NoSQL graph database named Neo4J with a popular relational database system, MySQL, which is used as the underlying technology in developing a software system. Query types are categorized into four groups: selection/ search, recursion, aggregation, and pattern matching. We examined representative questions for each group and executed them on Neo4j and MySQL using a real-life dataset named Career Village. The results show that Neo4j’s data query performance is better than MySQL in most results.