Specification-Driven Predictive Business Process Monitoring

TL;DR

This paper introduces a flexible, specification-driven approach for predictive business process monitoring, enabling the automatic creation of models for various prediction tasks based on user-defined specifications, demonstrated through real-life logs.

Contribution

It proposes a novel language for specifying diverse prediction tasks and an automated mechanism to generate corresponding models, broadening the scope beyond single-task focus.

Findings

Effective prediction model generation for multiple tasks

Successful application on real-life event logs

Enhanced flexibility in business process monitoring

Abstract

Predictive analysis in business process monitoring aims at forecasting the future information of a running business process. The prediction is typically made based on the model extracted from historical process execution logs (event logs). In practice, different business domains might require different kinds of predictions. Hence, it is important to have a means for properly specifying the desired prediction tasks, and a mechanism to deal with these various prediction tasks. Although there have been many studies in this area, they mostly focus on a specific prediction task. This work introduces a language for specifying the desired prediction tasks, and this language allows us to express various kinds of prediction tasks. This work also presents a mechanism for automatically creating the corresponding prediction model based on the given specification. Differently from previous studies, instead of focusing on a particular prediction task, we present an approach to deal with various prediction tasks based on the given specification of the desired prediction tasks. We also provide an implementation of the approach which is used to conduct experiments using real-life event logs.