SNAP: Semantic Stories for Next Activity Prediction
This addresses the need for better next activity prediction in business process management to optimize operations, but it is incremental as it builds on existing AI models by incorporating semantic information.
The paper tackled the problem of predicting the next activity in business processes by proposing SNAP, a method that uses semantic stories from event logs, and showed it significantly outperforms nine state-of-the-art models on six benchmark datasets, particularly for datasets with high semantic content.
Predicting the next activity in an ongoing process is one of the most common classification tasks in the business process management (BPM) domain. It allows businesses to optimize resource allocation, enhance operational efficiency, and aids in risk mitigation and strategic decision-making. This provides a competitive edge in the rapidly evolving confluence of BPM and AI. Existing state-of-the-art AI models for business process prediction do not fully capitalize on available semantic information within process event logs. As current advanced AI-BPM systems provide semantically-richer textual data, the need for novel adequate models grows. To address this gap, we propose the novel SNAP method that leverages language foundation models by constructing semantic contextual stories from the process historical event logs and using them for the next activity prediction. We compared the SNAP algorithm with nine state-of-the-art models on six benchmark datasets and show that SNAP significantly outperforms them, especially for datasets with high levels of semantic content.