## 6 Experiments  
This section describes simulations of an experiment by using CPN Tools [36]. We simulated the running state of WFT-net in Figure 4, and it corresponds to the motivation example shown in Figure 2. Among them, the black components represent the control flow process, which aligns with the control flow of the WFT-net in Figure 2, while the red components illustrate database tables and operations. To execute it, an engineer is required to input the preconditions in advance, as highlighted by the \"green\" section in Figure 4.  
In Figure 4, transitions operate on process data and database tables. Process data can be read, written, or deleted, while database data support selection, insertion, update, and deletion. For example, the initial tables \(R_{1}=(aid_{1}\), \(ac_{1})\) and \(R_{2}=(bid_{1}\), \(aid_{1}\), \(ac_{1})\) represent the red circles in Figure 4.  
Transitions involving persistent data operations are depicted as red boxes, with associated operations represented by red circles pointing to the respective transitions. For example, the operation \(upd\):\(bid\), \(aid\), \(ac\) on \(t_{14}\) in Figure 2 is shown in Figure 4 as a red circle labeled \(upd\):\(bid\), \(aid\), \(ac\), pointing to the red box \(t_{14}\). Similarly, transitions involving process data operations are shown as black circles with the respective operation pointing to the transition. For example, \(t_{8}\) with \(wt\):\(ac\) in Figure 2 is represented in Figure 4 as a black circle  
Figure 4: Illustration of the motivating example. WFT-net simulated in CPN Tools.  
In order to demonstrate the effectiveness of the proposed detection method in this paper, a set of comparative simulation experiments were conducted based on CPN Tools. The experiments were based on eight benchmarks: electronic commerce transaction (BM1), ER Triage (BM2) [29], company bid application (BM3), ShipHandle (BM4) [37], Travel (BM5) [38], InsuClaim (BM6) [39], Marketplace (BM7) [40], and write-up (BM8) [22].  
For each benchmark, we first modeled it with WF-net and ensured that the control-flows of its corresponding WFD-net and WFT-net were consistent. Considering the process data, persistent data, and corresponding operations, the WFD-net and WFT-net models were established, respectively. Since the inconsistency detection approach is not related to the recall rate, the experimental results of the eight case studies are presented in a tabular format for clarity and comparison. The details of such models and experimental results are shown in Table 2.  
Since WFT-net encompasses a richer array of data information, it excels in detecting a broader spectrum of data inconsistency errors compared to the WFD-net approach. Typically, WFD-net is adept at identifying data inconsistencies stemming from improper manipulation of data elements during concurrent transitions. Nevertheless, WFD-net falls short in capturing inconsistencies pertaining to database operations, including discrepancies in the values of shared attributes within a data log. Notably, these errors are crucial for assessing whether the modeled system harbors any design flaws, even if the system's operation remains uninterrupted. Consequently, the WFT-net model and its method for detecting data inconsistency hold significant value.