As IoT environments continue to grow in scale and complexity, the increasing number of interconnected sensors and devices makes end-to-end system behaviour progressively harder to understand. Process mining offers strong potential to address this challenge by transforming sensor-driven event data into interpretable insights at the process level. Yet, current tools are typically designed for business processes, not sensor-driven IoT workflows, which raises questions about their suitability in the IoT context. This discrepancy is evident in existing comparative studies, which often rely on feature checklists, rarely consider usability and interaction effort, or fail to evaluate support for domain-specific analytical tasks. This study introduces a structured evaluation methodology that combines a functional capability assessment derived from vendor materials with a task-based evaluation grounded in 13 representative questions from an IoT-oriented smart factory scenario, focusing on clarity, ease of use, and the ability to address context-specific analytical needs. The results highlight notable strengths and trade-offs among the investigated tools, demonstrating substantial variation in usability, effort, and analytical coverage, and showing that no single tool fully supports the breadth of process-intelligence needs in IoT contexts. The proposed methodology provides a replicable foundation for evaluating process mining tools in domain-specific settings and supports more informed tool selection for IoT-driven analytical workflows.
Tratnjek et al. (Wed,) studied this question.