## 4 The Proposed CIM-Oriented Ontology Database Framework for DMS
### The Procedure of Topology Processing Using an Ontology Database
These characteristics are advantageous for TP, and the time complexity in this case can be expressed as follows:  
\[O(h_{GDB})=O(n) \tag{3}\]  
The GDB demonstrates exceptional performance in handling large-scale interconnected data environments. This is attributed to its graph structure, which effectively represents data relationships and allows for efficient exploration. Figure 17 illustrates an example of TP using a GDB. As shown in Figure 17, it can be observed that the 'Terminal.ConductingEquipment' of '0' is '1' and the 'ConductingEquipment.Terminal' of '1' is '2'. The topology can be easily explored by iterating through this process. Starting from '0', one can sequentially explore 'S', 'P', and 'O' in the order of '1', '2', and '3' to find the next node. An RDB restricts index searches solely to primary keys, resulting in sluggish topology searches. On the contrary, an RDF triple store utilizes each triple (S, P, and O) as a primary key, and a GDB leverages the correlations of the RDF graph format, leading to rapid TP. Comparatively, the complexity of the system search varies; an RDB has a complexity of \(O(n^{2})\), while an RDF triple store and a GDB exhibit the complexities of \(O(n\log_{2}n)\) and \(O(n)\), respectively. As the number of nodes increases, the time complexity of an RDB  
Figure 17: Topology processing using a graph database. increases rapidly, resulting in poor topology processing performance. Equations (1)-(3) are theoretically derived to characterize the data traversal behavior of each database type in the context of TP within DMS platforms. These results will contribute to a more rigorous evaluation of system scalability and reliability under practical conditions. Although ODBs rely on semantic inference and graph-based processing, which may raise concerns about computational cost and slower query execution compared to traditional RDBs, this limitation is mitigated in the context of DMSs. Since power systems are inherently modeled as graph structures for topology-based applications such as load flow, fault detection, and network switching, the graph-oriented nature of ODBs provides structural alignment with DMS data models. In particular, the use of GDBs with index-free adjacency significantly enhances traversal speed without requiring complex join operations. Furthermore, semantic reasoning can be limited to offline or non-real-time processing tasks, enabling efficient and lightweight topology processing in real-time applications. This compatibility between domain structure and database design contributes to maintaining system performance while leveraging the flexibility and extensibility of ontology-based frameworks. The proposed framework supports scalability for large-scale real-time DMS platforms by leveraging the graph-based and schema-flexible properties of ODBs. In the case study, topology processing performance was evaluated using actual operational data from KEPCO's headquarters-level DMS. The system supports between 156 and 629 feeders per headquarters, depending on the region, validating the proposed framework's effectiveness in high-complexity, real-time conditions. This scalability is demonstrated through the case study results in Section 5.2 (Areas A, B, and C).