Outcome-Oriented Predictive Process Monitoring: Review and Benchmark

TL;DR

This paper reviews and benchmarks outcome-oriented predictive process monitoring methods, providing a systematic taxonomy and comparative evaluation of eleven methods across 24 real-life event log tasks.

Contribution

It offers the first comprehensive review and benchmark of outcome-oriented predictive process monitoring methods, standardizing evaluation and comparison.

Findings

Identified strengths and weaknesses of different methods

Provided a benchmark dataset for future research

Highlighted best-performing approaches for specific tasks

Abstract

Predictive business process monitoring refers to the act of making predictions about the future state of ongoing cases of a business process, based on their incomplete execution traces and logs of historical (completed) traces. Motivated by the increasingly pervasive availability of fine-grained event data about business process executions, the problem of predictive process monitoring has received substantial attention in the past years. In particular, a considerable number of methods have been put forward to address the problem of outcome-oriented predictive process monitoring, which refers to classifying each ongoing case of a process according to a given set of possible categorical outcomes - e.g., Will the customer complain or not? Will an order be delivered, canceled or withdrawn? Unfortunately, different authors have used different datasets, experimental settings, evaluation measures and baselines to assess their proposals, resulting in poor comparability and an unclear picture of the relative merits and applicability of different methods. To address this gap, this article presents a systematic review and taxonomy of outcome-oriented predictive process monitoring methods, and a comparative experimental evaluation of eleven representative methods using a benchmark covering 24 predictive process monitoring tasks based on nine real-life event logs.