Promoting Simple Agents: Ensemble Methods for Event-Log Prediction

2026-04-23 · Source: Takara TLDR - Daily AI Papers · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Expert, medium

Summary

A new study compares lightweight automata-based n-gram models with neural architectures like LSTM and Transformer for next-activity prediction in streaming event logs. Experiments on synthetic patterns and five real-world process mining datasets reveal that n-grams, when configured with appropriate context windows, achieve accuracy comparable to neural models while consuming significantly fewer computational resources. Unlike windowed neural architectures, which exhibit unstable performance, n-grams provide consistent accuracy. The research also explores classical ensemble methods like voting to enhance n-gram performance, noting that these methods increase memory and latency due to parallel agent execution during inference. To mitigate this, the authors propose a novel ensemble method, the "promotion algorithm," which dynamically selects between two active models during inference, thereby reducing overhead compared to traditional voting schemes. This new ensemble approach matches or surpasses the accuracy of non-windowed neural models on real-world datasets at a lower computational cost.

Key takeaway

For AI Engineers and Research Scientists developing event-log prediction systems, you should evaluate lightweight n-gram models as a viable alternative to neural networks. Their comparable accuracy and lower resource footprint, especially when combined with dynamic ensemble methods like the proposed "promotion algorithm," can lead to more efficient and stable deployments, particularly in streaming data environments where computational cost and latency are critical concerns.

Key insights

N-gram models offer competitive accuracy for event-log prediction with significantly fewer resources than neural networks.

Principles

• N-grams provide stable, consistent accuracy.
• Ensemble methods can improve n-gram performance.

Method

The "promotion algorithm" dynamically selects between two active models during inference, reducing overhead compared to classical ensemble voting schemes for event-log prediction.

In practice

• Use n-grams for resource-constrained event-log prediction.
• Consider dynamic ensemble methods for efficiency.

Topics

• Event-Log Prediction
• Ensemble Methods
• N-gram Models
• Neural Architectures
• Promotion Algorithm

Best for: AI Engineer, Research Scientist, AI Scientist, Machine Learning Engineer, MLOps Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Takara TLDR - Daily AI Papers.