# SNAP: Semantic Stories for Next Activity Prediction

arXiv: https://arxiv.org/abs/2401.15621 · HTML: https://mlsanity.com/paper/2401.15621/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 37 references that are in the map.
- First posted on arXiv 28 Jan 2024.

## Builds on (most cited first, 16 of 16)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://mlsanity.com/paper/2006.03654/) (2020)
- [TaBERT: Pretraining for Joint Understanding of Textual and Tabular Data](https://mlsanity.com/paper/2005.08314/) (2020)
- [Predictive Business Process Monitoring with LSTM Neural Networks](https://mlsanity.com/paper/1612.02130/) (2016)
- [Predicting Process Behaviour using Deep Learning](https://mlsanity.com/paper/1612.04600/) (2016)
- [Deep Learning for Predictive Business Process Monitoring: Review and Benchmark](https://mlsanity.com/paper/2009.13251/) (2020)
- [A systematic literature review on state-of-the-art deep learning methods for process prediction](https://mlsanity.com/paper/2101.09320/) (2021)
- [BINet: Multi-perspective Business Process Anomaly Classification](https://mlsanity.com/paper/1902.03155/) (2019)
- [Embedding Graph Convolutional Networks in Recurrent Neural Networks for Predictive Monitoring](https://mlsanity.com/paper/2112.09641/) (2021)
- [A Comparison of Deep-Learning Methods for Analysing and Predicting Business Processes](https://mlsanity.com/paper/2102.07838/) (2021)
- [Exploiting Event Log Event Attributes in RNN Based Prediction](https://mlsanity.com/paper/1904.06895/) (2019)
- [A Conversational Digital Assistant for Intelligent Process Automation](https://mlsanity.com/paper/2007.13256/) (2020)
- [An Explainable Decision Support System for Predictive Process Analytics](https://mlsanity.com/paper/2207.12782/) (2022)
- [Prescriptive Process Monitoring in Intelligent Process Automation with Chatbot Orchestration](https://mlsanity.com/paper/2212.06564/) (2022)

## Built on by (most cited first, 8 of 8)

- [ST-WebAgentBench: A Benchmark for Evaluating Safety and Trustworthiness in Web Agents](https://mlsanity.com/paper/2410.06703/) (2024)
- [AgentFixer: From Failure Detection to Fix Recommendations in LLM Agentic Systems](https://mlsanity.com/paper/2603.29848/) (2026)
- [From Benchmarks to Business Impact: Deploying IBM Generalist Agent in Enterprise Production](https://mlsanity.com/paper/2510.23856/) (2025)
- [LLMs that Understand Processes: Instruction-tuning for Semantics-Aware Process Mining](https://mlsanity.com/paper/2508.16270/) (2025)
- [RLHGNN: Reinforcement Learning-driven Heterogeneous Graph Neural Network for Next Activity Prediction in Business Processes](https://mlsanity.com/paper/2507.02690/) (2025)
- [Governance by Construction for Generalist Agents](https://mlsanity.com/paper/2605.20874/) (2026)
- [Textual Planning with Explicit Latent Transitions](https://mlsanity.com/paper/2602.04557/) (2026)
- [EHHN: An Event-driven Heterogeneous Hypergraph Network for Object-Centric Next Activity Prediction](https://mlsanity.com/paper/2607.01785/) (2026)

## Abstract (arXiv)

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.