Abstract Predictive process monitoring is a subfield of process mining that aims to estimate case or event features for running process instances. Such predictions are of significant interest to the process stakeholders. However, most of the state-of-the-art methods for predictive monitoring require the training of complex machine learning models, which is often inefficient. Moreover, most of these methods require a hyper-parameter optimization that requires several repetitions of the training process which is not feasible in many real-life applications. In this paper, we propose an instance selection procedure that allows sampling training process instances for prediction models. We show that our instance selection procedure allows for a significant increase of training speed for next activity and remaining time prediction methods while maintaining reliable levels of prediction accuracy.

Outcome-oriented predictive process monitoring aims at classifying a running process execution according to a given set of categorical outcomes, leveraging data on past process executions. Most previous studies employ Recurrent Neural Networks to encode the sequence of events, without taking the structure of the process into account. However, process executions typically involve complex control-flow constructs, like parallelism and loops. Different executions of these constructs can be recorded as different event sequences in the event log. This makes it challenging for a recurrent classifier to detect potential relations between a high-level control-flow construct and the prediction target. This is especially true in the presence of high variability in process executions and lack of data. In this paper, we propose a novel approach which encodes the control-flow construct each event belongs to. First, we exploit Local Process Model mining techniques to extract frequently occurring control-flow patterns from the event log. Then, we employ different encoding techniques to enrich an on-going process execution with information related to the extracted control-flow patterns. We tested the proposed method on nine real-life event logs. The obtained results show consistent improvements in the prediction performance.