Process automation is used to increase the performance of processes. One of the leading process automation tools is Microsoft Process Advisor. This tool requires users to select the corresponding connectors for the automation of different tasks, which can be a challenging endeavor for users who have limited business knowledge as there are various connectors and templates exist. To overcome this challenge, we present a process-aware recommender system for connectors that eases the labeling task for end users. The results of applying this method to real event logs indicate that it can recommend relevant connectors and, therefore, the usage of the same mechanism might be generalized to broader contexts.

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.

Conformance checking techniques are used to compute to what degree a process model and real execution data correspond to each other. In recent years, alignments have proven to be useful for calculating conformance statistics. Most alignment techniques provide an exact conformance value. However, in many applications, it suffices to have an approximated alignment value. Specifically, for large event data and using standard hardware, current alignment techniques are time-consuming and sometimes intractable. This paper proposes to use simulated behaviors of process models to approximate the conformance checking value. To simulate a process model, we exploit the behavior in the given event data. This method is independent from the process model notation and provides upper and lower bounds for the approximated alignment value. We assess the quality of our approximations and compare it to existing approximation techniques. The experiments on real event data show that using the proposed method, it is possible to achieve significant performance improvements.