Abstract
Predictive process monitoring is concerned with predicting measures of interest for a running case (e.g., a business outcome or the remaining time) based on historical event logs. Most of the current predictive process monitoring approaches only consider intra-case information that comes from the case whose measures of interest one wishes to predict. However, in many systems, the outcome of a running case depends on the interplay of all cases that are being executed concurrently. For example, in many situations, running cases compete over scarce resources. In this paper, following standard predictive process monitoring approaches, we employ supervised machine learning for prediction. In particular, we present a method for feature encoding of process cases that relies on a bi-dimensional state space representation: the first dimension corresponds to intra-case dependencies, while the second dimension reflects inter-case dependencies to represent shared information among running cases. The inter-case encoding derives features based on the notion of case types that can be used to partition the event log into clusters of cases that share common characteristics. To demonstrate the usefulness and applicability of the method, we evaluated it against two real-life datasets coming from an Israeli emergency department process, and an open dataset of a manufacturing process.