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<p>Process Mining is a computational discipline aimed at discovering, monitoring, and improving processes. In the business sector, it integrates artificial intelligence and data mining to uncover trends, patterns, and insights within classified data systems. The Trace Alignment algorithm is a key tool in this field, designed to detect anomalies and identify similarities in event sequences. However, most implementations rely on progressive alignment approaches, which are computationally expensive. This paper introduces a novel Trace Alignment implementation with significant theoretical enhancements. By incorporating advanced programming techniques and parallel computing, the algorithm achieves polynomial time complexity, a notable improvement over previous methods. Validation results confirm its efficiency and the alignment’s high quality.</p>

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Trace alignment algorithm optimization

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年8月1日

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  • Leandro González-Montesino,
  • Darian H. Grass-Boada

摘要

Process Mining is a computational discipline aimed at discovering, monitoring, and improving processes. In the business sector, it integrates artificial intelligence and data mining to uncover trends, patterns, and insights within classified data systems. The Trace Alignment algorithm is a key tool in this field, designed to detect anomalies and identify similarities in event sequences. However, most implementations rely on progressive alignment approaches, which are computationally expensive. This paper introduces a novel Trace Alignment implementation with significant theoretical enhancements. By incorporating advanced programming techniques and parallel computing, the algorithm achieves polynomial time complexity, a notable improvement over previous methods. Validation results confirm its efficiency and the alignment’s high quality.