# Investigating Social Contextual Factors
## 2 Materials and Methods
### Predictive Monitoring
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{4}{c}{**\% of Complete Traces in Event Log**} \\ \cline{2-6}  & **0\%** & **20\%** & **40\%** & **60\%** & **80\%** \\ \hline BPIC 12 & **1.2 × 10\({}^{-7}\)** & **0.0003** & 0.1158 & 0.0737 & 0.0909 \\ \hline BPIC 18 & **<2 × 10\({}^{-16}\)** & **<2 × 10\({}^{-16}\)** & **<2 × 10\({}^{-16}\)** & **2.5 × 10\({}^{-16}\)** & **6.0 × 10\({}^{-12}\)** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Pairwise Comparisons against the baseline using Wilcoxon rank-sum test. The final threat to validity is related to the choice of grouped centrality measures selected as social contextual. We selected the most widely used centrality measures in the literature [18; 21]. We, however, acknowledge that there are additional grouped centrality measures that we could have included (e.g., diffusion and fragmentation centrality) which may have shed further insight. We intend to explore the impact of these in future research studies. ## 4 Conclusions

This study has proposed an approach to censor an event log to facilitate its use for building a survival function. We explored the impact of social contextual factors as covariates in the survival function. We found that group betweenness and closeness centrality were generally positively correlated while the group eigenvector centrality was generally negatively correlated. We also found that survival analysis approaches perform comparably with start-of-the-art predictive process monitoring techniques. In terms of further research, we propose a couple of areas for further exploration. Firstly, as an increasing number of automated agents augment the human workforce, we believe there is value in exploring how social networks between these categories of workers differ and the implications of these. Secondly, we propose an exploration of the effect the feature values of neighbouring nodes have on behaviour. For example, we could explore the temporal effect of a high workload on workers in an individual or group's neighbourhood. This would enable a better understanding of workload distribution in the social network over time. In future work, we intend to attempt to tackle a number of these opportunities. Conceptualization, N.O. ; methodology, N.O. ; software, N.O. validation, A.B. and T.C. ; formal analysis, N.O. ; investigation, N.O. ; resources, N.O., A.B. and T.C. ; data curation, N.O. ; writing--original draft preparation, N.O. ; writing--review and editing, A.B. and T.C. ; visualization, N.O. ; supervision, A.B. and T.C. ; project administration, N.O. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The authors declare no conflict of interest. ## References

* (1) [PERSON] _Process Mining: Data Science in Action_, 2 nd ed. ; Springer: Berlin/Heidelberg, Germany, 2016. * (2) [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON] Predictive business process monitoring with LSTM neural networks. In Proceedings of the International Conference on Advanced Information Systems Engineering, Essen, Germany, 12-16 June 2017; pp. 477-492. * (3) [PERSON] Combining Process Mining and Queueing Theory for the ICT Ticket Resolution Process at LUMC. Master's Thesis, University of Twente, Enschede, The Netherlands, 2017. * (4) [PERSON] [PERSON]; [PERSON] Prediction of remaining service execution time using stochastic petri nets with arbitrary firing delays. In _International Conference on Service-Oriented Computing_; Springer: Berlin/Heidelberg, Germany, 2013; pp. 389-403. * (5) [PERSON] Non-parametric survival analysis. _Stat. Sci._**2004**, _19_, 615-623. [CrossRef]
* (6) [PERSON] Modelling employee withdrawal behaviour over time: A study of turnover using survival analysis. _J. Occup. Organ. Psychol._**1996**, _69_, 315-326. [CrossRef]
* (7) [PERSON]; [PERSON] [PERSON] Investigating the role of product features in preventing customer churn, by using survival analysis and choice modeling: The case of financial services. _Expert Syst. Appl._**2004**, _27_, 277-285.