Rectify Sensor Data in IoT: A Case Study on Enabling Process Mining for Logistic Process in an Air Cargo Terminal

The Internet of Things (IoT) has empowered enterprises to optimize process efficiency and productivity by analyzing sensor data. This can be achieved with process mining, a technology that enables organizations to extract valuable insights from data recorded during process execution, referred to as event data in a process mining context. In our case study, we aim to apply process mining to sensor data collected within a logistic process at an air cargo terminal, specifically from device-to-device communication. By representing the sensor data as event data, we rectify them to accurately capture the movement of package distribution in the logistic process. However, due to the communication dynamics, challenges arise from the presence of irrelevant data that does not impact the process instance’s status. Moreover, issues such as faulty sensor readings and ambiguous data interpretation further compound these challenges. To overcome the obstacles, we collaborate with domain experts to develop rules that take into account the context of each event in a trace, enabling us to effectively capture package distribution within the system. We present the results of our process mining analysis, which have been validated by domain experts. This case study contributes to the understanding and utilization of sensor data for process mining in IoT environments, with a specific focus on data collected from device-to-device communication.