# Investigating Social Contextual Factors
## 2 Materials and Methods
### Predictive Monitoring  
The approach consists of two phases: offline and online (see Figure 2). In the offline phase, the traces in the event log are encoded. We utilise a novel encoding approach, where we compute the grouped centrality measures for each trace (see Definition 11) using all the performers (#performer(\(e_{1}\))... # performer(\(e_{n}\))) associated with that trace as well as the start and end event activity labels. Figure 2: Overview of the proposed approach. While we adopt that approach to explore the impact of social contextual factors on process cycle time, we acknowledge that other encoding approaches, such as index-based encoding which is \"lossless\" ([PERSON], 2017), could also be equally adopted. Our approach is in effect a combination of aggregation and last state encoding ([PERSON] and [PERSON], 2018) where the aggregation function computes the group degree, betweenness, closeness, and eigenvalue centrality for each trace based on the set of performers who executed the events in that trace. This approach enables us to treat the performers who execute the activities in a trace as a team and builds on the approach in \"the team effectiveness literature where researchers have used several internal team composition variables to predict performance\" ([PERSON] and [PERSON], 2018). We utilise the parametric Weibull model to build the survival model. Even though it requires that certain assumptions regarding the distribution of the process cycle time are satisfied, this method offers several unique benefits in that it is \"simultaneously both proportional and accelerated so that both relative event rates and relative extension in\" process cycle \"time can be estimated\" ([PERSON] and [PERSON], 2018). In the online phase, the in-flight traces are encoded utilising the same approach as in the online phase. The survival model built in the offline phase is used to estimate the total cycle time for the trace, and the remaining time for the trace is computed by subtracting the elapsed time from the estimated cycle time. Algorithm 1 details the survival analysis predictive modelling algorithm. ``` Input:

### Evaluation

In this section, we perform two sets of experiments to address the research questions of interest in this study. In the first set of experiments, we evaluate the impact of social contextual features on the cycle time of process traces. In the second set of experiments, we evaluate the proposed survival analysis predictive monitoring techniques against similar predictive monitoring techniques. Specifically, we seek to address the following research questions:

**RQ1:** What is the relationship between social contextual factors and process completion time? **RQ2:** How does the survival analysis predictive process monitoring approach compare with existing approaches? In the following section, we provide further details about the experimental setup and how we answer the research questions. #### 2.5.1 Datasets

Five real-life event logs from the Business Process Intelligence Challenge (BPIC) were used for the experiments as follows: BPLC12 ([PERSON] and [PERSON], 2018), BPIC14 ([PERSON] and [PERSON], 2018), BPIC15(3) ([PERSON] and [PERSON], 2018), BPIC17 ([PERSON] and



The logs were from a variety of domains covering diverse processes. To manage memory requirements, a subset of each event log (except for [24; 26] where the entire log was used) was selected for the analysis. The event logs were selected on the basis that information about the performer (i.e., the individual resource or team) that executed each event (#performer (\(e\))) is present in the log. This enabled us to create the social networks required to address the research questions. For example, [29] is not suitable as the resource attribute is abstracted to a high-level role (e.g., staff member). Moreover, basic feature engineering was performed to add required features such as elapsed time and remaining time to each log. See Table 1 for a summary of the logs used for the experiments.