Positional trace encoding for next activity prediction in event logs

The analysis of log data, generated by running processes in many application domains, enables organizations to identify opportunities for operational improvements. For instance, in healthcare, analyzing patient treatment logs can optimize care pathways; in manufacturing, production line logs can reveal bottlenecks; and in customer service, ticket resolution logs can streamline response protocols. One key analytical task is predicting the next activity in a process, which supports operational decision-making through better resource allocation and proactive response to customer needs. In this paper, we solve the next activity prediction task by exploiting a novel positional encoding approach that is based on sliding windows. This approach allows us to consider both a way to adapt to changes in the data distribution, and exploit positional information of the activities in the traces. The method proposed in this paper, called OREO, takes into account these aspects through a positional encoding tightly coupled with specific types of deep neural network architectures. The results on eight real-world process logs show the superiority of the models exploiting OREO encoding over state-of-the-art approaches, confirming our initial intuition of benefits gained by combining a time-window based model with positional information.