arXiv:2606.15868v1 Announce Type: new Abstract: Next activity prediction (NAP) is a cornerstone of predictive process monitoring (PPM), enabling organizations to move from retrospective analysis to proactive process steering. The PPM field has progressed from classical machine learning through deep learning architectures such as LSTMs and Transformers to large language models (LLMs). Despite growing model complexity, no benchmark jointly compares LLMs, Transformers, LSTMs, and simple baselines in a direct sequence modeling setting for NAP. In this paper, we fill this gap with a systematic benc
The proliferation of advanced AI models (LLMs, Transformers, LSTMs) necessitates a clear benchmark for their real-world applicability in process monitoring, which this paper aims to provide.
This research provides critical insights into the performance trade-offs between complex deep learning models and simpler baselines for a fundamental task in predictive process monitoring, influencing adoption and development strategies.
The systematic comparison could lead to more efficient and effective deployment of AI in business process management, potentially favoring simpler models where complexity provides diminishing returns.
- · Businesses adopting predictive process monitoring
- · Developers of lightweight AI models
- · Process optimization software vendors
- · Overly complex or undifferentiated deep learning solutions
- · Organizations with opaque AI selection processes
Improved efficiency and accuracy in predictive process monitoring across various industries.
A re-evaluation of model complexity and computational resource allocation in industrial AI applications.
Enhanced automation and proactive decision-making frameworks, potentially leading to new business models and services.
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Read at arXiv cs.LG