Incorporating spatial context into remaining-time predictive process monitoring

Predictive business process monitoring aims to accurately predict a variable of interest (e.g. remaining time) or the future state of the process instance (e.g. outcome or next step). It is an important topic both from a research and practitioner perspective. For example, existing research suggests that even when problems occur with service provision, providing accurate estimates around process completion time is positively correlated with increasing customer satisfaction. The quest for models with higher predictive power has led to the development of a variety of novel techniques. However, though the location of events is a crucial explanatory variable in many business processes, as yet there have been no studies which have incorporated spatial context into the predictive process monitoring framework. This paper seeks to address this problem by introducing the concept of a spatial event log which records location details at a trace or event level. The predictive utility of spatial contextual features is evaluated vis-à-vis other contextual features. An approach is proposed to predict the remaining time of an in-flight process instance by calculating the buffer distances between the location of events in a spatial event log to capture spatial proximity and connectedness. These distances are subsequently utilised to construct a regression model which is then used to predict the remaining time for events in the test dataset. The proposed approach is benchmarked against existing approaches using five real-life event logs and demonstrates that spatial features improve the predictive power of business process monitoring models