## 7 Case Study
In conclusion, this step realizes the visualization and interoperation of the IFC information model which is difficult to understand and operate. This step lays a good foundation for the following analysis. Figure 10: Graph database-based information model. Analyzing the Graph Database-Based and IFC-Based 4D Construction Management Information Model of Engineering Case's Prefabricated Building  
1. Data Update. The user's operation of complex IFC files has been transformed into an easy-to-operate graph database. Since data update runs through the entire construction management and is described in detail in Section 6, it will not be repeated here. 2. Schedule Network Analysis. This paper analyzes the complex schedule network of this engineering case. The degree centrality, closeness centrality, and betweenness centrality of this schedule network are calculated by Formulas (1)-(3). The top 10 construction tasks in numerical value are shown in Table 8.  
From the calculated results, we can see that Task 74 ranks first in all three parameters, indicating that it has a very important impact on the whole schedule construction network. In other words, if Task 74 is delayed, it will cause a large number of tasks in the schedule network to be delayed. It brings great risk for the whole construction schedule delay. With this information, managers should pay more attention to Task 74 and manage it better to avoid this task's delay. This crucial information will be stored in the extended property set Pset_ScheduleNetworkAnalysis and associated with the corresponding IfcTask entities. The presentation of information from Task 74 and its associated datasets in the graph data is shown in Figure 11. The blue node representing Task 74 is associated with the pink node representing IfcPropertySet, including Pset_CriticalPath, Pset_ScheduleNetworkAnalysis, and Pset_DelayAnalysis. 3. Delay Analysis. According to the schedule information and schedule network automatically created in the graph database, this paper uses the graph's algorithm to calculate the critical path. The critical path and its property are shown in Figure 12. In this figure, blue nodes represent IfcTask entities, green nodes represent IfcTaskTime, orange nodes represent IfcRelSequence, pink nodes represent IfcPropertySet named Pset_CriticalPath, and purple nodes represent IfcProperty. The IfcTask-IfcRelSequence-IfcTask line is the calculated critical path. Moreover, this paper also calculates every task's significant properties by using the graph's character and updating this information in the graph. These significant properties include Earliest Finish Time (EF), Earliest Start Time (ES), Latest Start Time (LS), Latest Finish Time (LF), and Slack Time. Once the tasks' data collected in the actual construction illustrated delay is compared to the scheduled time, the program analyzes the delay. In this engineering case, Task 74 was completed three days later than it was expected to be completed. Based on the calculation of the graph, it will be immediately analyzed that the total construction period will be delayed by three days, thus facilitating the decision-maker's control of the construction period. In addition, Task 74 will be associated with the extended property set Pset_DelayAnalysis, as shown in Figure 11.  
4. 4D visualization. Based on the above applications, the construction management information model with the time domain can finally be created. The graph database-based approach clarifies the association relationship between construction task entities and related information in the IFC information model. As mentioned before, the construction