Figure 5 Example process model. Highlighted is the current marking
Related Figures (18)
FIGURE 1 Core BPMN elements 27. synchronization of the multiple incoming paths 27. A process model can be decomposed into process fragments. A pro- TABLE 1 Extract of an event log. A wide range of process automated process discovery algorithms have been proposed in the literature 2’, with Petri nets and BPMN being the most common output model formats. processes, the waiting time makes up a considerable proportion of the FIGURE 2 Typical process model patterns: sequential (a), XOR-block (b), AND-block (c) and rework loop (d). that the rework fragment will be executed follows a geometric distribu- tion with the expected value 1/(1 — r). Thus, the average cycle time of the fragment in this case is: FIGURE 3 Overview of the proposed approach. process, generally we need to discover the model from event logs. For FIGURE 4 Backtracking algorithm (taken from*?). backtracks to the parent node to traverse other branches. FIGURE 6 Unfolding the rework loop of F TABLE 3 Training set to predict the branching probabilities of X32 (single model). 1.6.2 | Feature encoding with multiple predictive models In this work, we will use the former approach, as we are mostly inter ested in predictions in the early stages of a process evolution wher many process instances have not finished yet, so there is still sufficien amount of data to train the predictive models. In this work, we will use the former approach, as we are mostly inter- TABLE 2 Training set to predict the cycle time of F' (single model). TABLE 6 Statistics of the datasets used in the experiments. TABLE 8 Weighted average MAE over all prefixes. TABLE 7 Learning parameters of XGBoost that have been tuned. FIGURE 8 Average ranking of the evaluated methods over all datasets. Error bars indicate the 95% confidence interval. FIGURE 7 Prediction accuracy (measured in terms of MAE) across different prefix lengths. TABLE 9 MAE of cycle time predictions of individual activities and their actual mean cycle times (in days). TABLE 10 Predicted and average values of cycle times and branching probabilities for hd? (SWKD). Execution Times FIGURE 10 Normalized standard deviation of MAE over all prefixes FIGURE 9 Normalized MAE averaged over all prefixes and all datasets. FIGURE 11 A process model of the Hospital log. Current marking of hd? (SWKD) and its predicted future path are highlighted.
