mlsanityData to 14 Oct 2026

SNAP: Semantic Stories for Next Activity Prediction

arXiv 2401.15621 ↗First posted 28 Jan 2024cs.AIcited by 8

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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.

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

16 of 37 references.

References · 16

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4 more
  1. 2019Exploiting Event Log Event Attributes in RNN Based Predictioncited by 8
  2. 2020A Conversational Digital Assistant for Intelligent Process Automationcited by 8
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 3; 2026: 4.

8 papers.

  1. 2024ST-WebAgentBench: A Benchmark for Evaluating Safety and Trustworthiness in Web Agentscited by 133
  2. 2026AgentFixer: From Failure Detection to Fix Recommendations in LLM Agentic Systemscited by 7
  3. 2025From Benchmarks to Business Impact: Deploying IBM Generalist Agent in Enterprise Productioncited by 5
  4. 2025LLMs that Understand Processes: Instruction-tuning for Semantics-Aware Process Miningcited by 2
  5. 2025RLHGNN: Reinforcement Learning-driven Heterogeneous Graph Neural Network for Next Activity Prediction in Business Processescited by 1
  6. 2026Governance by Construction for Generalist Agentscited by 1
  7. 2026Textual Planning with Explicit Latent Transitionscited by 0
  8. 2026EHHN: An Event-driven Heterogeneous Hypergraph Network for Object-Centric Next Activity Predictioncited by 0