## 2 Background and Definition of CIM  
### Background of CIM  
As global urbanization accelerates at an unprecedented rate, metropolitan areas are confronting multifaceted challenges encompassing traffic gridlock, environmental degradation, and suboptimal resource utilization. Conventional urban governance approaches frequently prove inadequate in addressing these complex issues, constrained by fragmented data silos and the lack of a unified data-driven governance framework [6; 7]. To bridge this technological gap, CIM has evolved into a systematic framework that synergizes multi-source heterogeneous data integration with advanced computational analytics to enable intelligent urban planning and digital governance. Distinct from isolated technological solutions, CIM establishes a cyber-physical system through the convergence of a GIS, BIM,IoT sensor streams, and urban operation records. This convergence enables cross-domain data interoperability and computational simulation capabilities, ultimately constructing a dynamic digital twin that supports 3D visualization, real-time interaction, and predictive analytics for urban systems. The technical architecture enhances decision-making precision in critical domains ranging from infrastructure planning to emergency management through its data fusion mechanisms and AI-powered analytical pipelines. The evolution of CIM originates from the technological convergence in the 1990s, when the integration of a GIS and BIM laid the groundwork for digital urban management [8; 9]. While a GIS delivered spatial-temporal analytics through georeferenced datasets, BIM provided semantically rich digital twins of architectural entities. Nevertheless, these systems initially operated as isolated technological silos with limited cross-platform data interoperability and collaborative computation capabilities. The conceptualization of \"digital cities\" catalyzed the systematic digitization of urban infrastructures and environmental systems, marking a paradigm shift toward data-centric urban governance. Early implementations prioritized geospatial visualization and physics-based simulation, yet constrained by primitive data integration architectures that impeded real-time complex decision-support systems [9; 10]. The proliferation of information and communication technologies (ICT) subsequently propelled urban management systems toward intelligent transformation. Critical technological enablers--including IoT sensor networks, distributed cloud computing, and scalable big data analytics--facilitated the establishment of data-driven governance frameworks, thereby advancing CIM's architectural maturity. Through progressive BIM-GIS integration and computational ontology development, CIM evolved into an academically validated and industrially adopted paradigm [9; 10]. This phase emphasized the multi-scale spatiotemporal data fusion across urban infrastructure networks, ecological monitoring systems, and socioeconomic datasets, while incorporating machine learning pipelines to optimize the urban operational intelligence. Recent breakthroughs in 5G edge computing and deep neural networks have substantially augmented CIM's cyber-physical implementation capabilities [11]. These technological accelerators empower CIM architectures with distributed edge intelligence for real-time streaming analytics, enabling dynamic urban digital twin simulations, probabilistic predictive modeling, and autonomous decision-support systems. As these computational infrastructures mature, CIM has emerged as a mission-critical component in smart city ecosystems, delivering enhanced precision in urban planning optimization and service automation. Despite these advancements, the data governance ecosystem persists as a critical implementation bottleneck for CIM. Technical challenges including heterogeneous data schema integration, quality assurance mechanisms, and cross-domain governance protocols directly determine CIM's operational efficacy.