With the increasing complexity of production technologies, alarm management becomes more and more important in industrial process control. The overall safety of the plant relies heavily on the situation-aware response time of the staff. This kind of awareness has to be supported by a state-of-the-art alarm management system, which requires broad and up-to-date process-relevant knowledge. The proposed method provides a solution when such information is not fully available. With the utilization of machine learning algorithms, a real-time event scenario prediction can be gained by comparing the frequent event patterns extracted from historical event-log data with the actual online data stream. This study discusses an integrated solution, which combines sequence compression and sequence alignment to predict the most probable alarm progression. The effectiveness and limitations of the proposed method are tested using the data of an industrial delayed-coker plant. The results confirm that the presented parameter-free method identifies the characteristic patternsoperational statesand their progression with high confidence in real time, suggesting it for a wider adoption for sequence analysis.