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<p>Detecting pattern matches underpins key operations across fields, such as complex event processing (CEP), sequential pattern mining (SPM), string pattern matching, pattern mining from a large sequence, and business process mining. These fields employ various notations and definitions for the detected patterns, posing challenges in recognizing their shared underlying concepts. This work aims to bridge these gaps by proposing a unified notation and terminology and then cataloging various pattern queries and constraints identified in different fields into a comprehensive framework. Our analysis reveals substantial similarities among the various pattern types, suggesting a promising avenue for the transfer of techniques between disciplines. This approach paves the way to leverage existing knowledge efficiently and circumvent the redundancy of “reinventing the wheel”.</p>

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Sequential pattern detection: similarities and differences across various fields

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年6月6日

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  • Ioannis Mavroudopoulos,
  • Kostas Tsichlas,
  • Anastasios Gounaris

摘要

Detecting pattern matches underpins key operations across fields, such as complex event processing (CEP), sequential pattern mining (SPM), string pattern matching, pattern mining from a large sequence, and business process mining. These fields employ various notations and definitions for the detected patterns, posing challenges in recognizing their shared underlying concepts. This work aims to bridge these gaps by proposing a unified notation and terminology and then cataloging various pattern queries and constraints identified in different fields into a comprehensive framework. Our analysis reveals substantial similarities among the various pattern types, suggesting a promising avenue for the transfer of techniques between disciplines. This approach paves the way to leverage existing knowledge efficiently and circumvent the redundancy of “reinventing the wheel”.