The integration of the Internet of Things (IoT) into various industries has led to an exponential increase in the volume of data generated, posing significant challenges for compliance monitoring. Traditional compliance checking methods are often inadequate due to their inability to handle the high velocity and volume of real-time data streams emanating from IoT devices. In this paper, we introduce a novel distributed computing framework specifically designed to address these challenges by leveraging process mining techniques on IoT log data streams. The proposed framework is built to scale horizontally, allowing for the parallel processing of massive data sets while maintaining real-time performance. It is capable of identifying compliance patterns, detecting deviations, and providing actionable insights to ensure that IoT systems operate within the confines of established regulations and standards. To evaluate the performance and effectiveness of the proposed framework, we conducted extensive experiments using a publicly available industrial IoT log dataset. The results demonstrate the framework’s ability to process large volumes of data in real-time, offering a significant improvement over existing centralized approaches.