Automata Learning versus Process Mining: The Case for User Journeys

2026-04-04

AI Summary

This study addresses the challenge of automatically constructing high-fidelity user journey models from interaction logs between users and digital services. The authors propose a novel hybrid approach that integrates automata learning with process mining techniques. A key innovation is the introduction of an adaptive algorithm selection mechanism, which dynamically chooses the optimal modeling strategy based on the characteristics of the event log. This mechanism effectively mitigates the limitations of each individual technique—namely, their reliance on expert knowledge or assumptions about specific event distributions. Empirical evaluation on real-world datasets demonstrates that the proposed hybrid method significantly outperforms either technique used in isolation, yielding substantially more accurate user journey models.

Abstract

With the servitization of business, understanding how users experience services becomes a crucial success factor for companies. Therefore, there is a need to include feedback from user experiences in the software engineering process. Behavioral models of user journeys, describing how users experience their interaction with a service, can provide insights and potentially improve services. In this paper, we investigate techniques that allow the automatic generation of behavioral models from user interactions with a service, recorded in an event log. We first compare two established techniques that generate behavioral models from a given event log: automata learning and process mining. Afterward, we present a novel, hybrid method that combines both automata learning and process mining methods to overcome their limitations. For the existing techniques, we present methods to learn models of user journeys and evaluate the accuracy of the resulting models. We then compare these techniques with our novel method for the automatic extraction of user journey models from the event logs of digital services. We assess the practical applicability of all techniques by evaluating real-world applications. Our results show that process mining techniques rely on expert knowledge, while automata learning techniques depend on the distribution of events in the given event log. We further show that the proposed hybrid technique combines the strengths of both process mining and automata learning, automatically selecting the best method and parameter settings for a given event log to learn very accurate models.

Problem

• user journeys
• behavioral models
• event logs
• automata learning
• process mining

Innovation

• automata learning
• process mining
• user journey modeling
• hybrid method
• event log