SNAP: Semantic Stories for Next Activity Prediction

TL;DR

SNAP is a novel method that uses language foundation models to create semantic stories from process logs, significantly improving next activity prediction accuracy in business process management.

Contribution

The paper introduces SNAP, a new approach leveraging semantic contextual stories and language models for enhanced activity prediction in BPM.

Findings

SNAP outperforms nine state-of-the-art models on benchmark datasets.

SNAP is especially effective with datasets rich in semantic content.

Semantic stories improve prediction accuracy in process logs.

Abstract

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.