{"title":"重新思考人工智能代理时代的流程挖掘","authors":"Alessandro Berti, Mayssa Maatallah, Urszula Jessen, Michal Sroka, Sonia Ayachi Ghannouchi","doi":"arxiv-2408.07720","DOIUrl":null,"url":null,"abstract":"Large Language Models (LLMs) have emerged as powerful conversational\ninterfaces, and their application in process mining (PM) tasks has shown\npromising results. However, state-of-the-art LLMs struggle with complex\nscenarios that demand advanced reasoning capabilities. In the literature, two\nprimary approaches have been proposed for implementing PM using LLMs: providing\ntextual insights based on a textual abstraction of the process mining artifact,\nand generating code executable on the original artifact. This paper proposes\nutilizing the AI-Based Agents Workflow (AgWf) paradigm to enhance the\neffectiveness of PM on LLMs. This approach allows for: i) the decomposition of\ncomplex tasks into simpler workflows, and ii) the integration of deterministic\ntools with the domain knowledge of LLMs. We examine various implementations of\nAgWf and the types of AI-based tasks involved. Additionally, we discuss the\nCrewAI implementation framework and present examples related to process mining.","PeriodicalId":501123,"journal":{"name":"arXiv - CS - Databases","volume":"60 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Re-Thinking Process Mining in the AI-Based Agents Era\",\"authors\":\"Alessandro Berti, Mayssa Maatallah, Urszula Jessen, Michal Sroka, Sonia Ayachi Ghannouchi\",\"doi\":\"arxiv-2408.07720\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Large Language Models (LLMs) have emerged as powerful conversational\\ninterfaces, and their application in process mining (PM) tasks has shown\\npromising results. However, state-of-the-art LLMs struggle with complex\\nscenarios that demand advanced reasoning capabilities. In the literature, two\\nprimary approaches have been proposed for implementing PM using LLMs: providing\\ntextual insights based on a textual abstraction of the process mining artifact,\\nand generating code executable on the original artifact. This paper proposes\\nutilizing the AI-Based Agents Workflow (AgWf) paradigm to enhance the\\neffectiveness of PM on LLMs. This approach allows for: i) the decomposition of\\ncomplex tasks into simpler workflows, and ii) the integration of deterministic\\ntools with the domain knowledge of LLMs. We examine various implementations of\\nAgWf and the types of AI-based tasks involved. Additionally, we discuss the\\nCrewAI implementation framework and present examples related to process mining.\",\"PeriodicalId\":501123,\"journal\":{\"name\":\"arXiv - CS - Databases\",\"volume\":\"60 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-08-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"arXiv - CS - Databases\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/arxiv-2408.07720\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - CS - Databases","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2408.07720","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Re-Thinking Process Mining in the AI-Based Agents Era
Large Language Models (LLMs) have emerged as powerful conversational
interfaces, and their application in process mining (PM) tasks has shown
promising results. However, state-of-the-art LLMs struggle with complex
scenarios that demand advanced reasoning capabilities. In the literature, two
primary approaches have been proposed for implementing PM using LLMs: providing
textual insights based on a textual abstraction of the process mining artifact,
and generating code executable on the original artifact. This paper proposes
utilizing the AI-Based Agents Workflow (AgWf) paradigm to enhance the
effectiveness of PM on LLMs. This approach allows for: i) the decomposition of
complex tasks into simpler workflows, and ii) the integration of deterministic
tools with the domain knowledge of LLMs. We examine various implementations of
AgWf and the types of AI-based tasks involved. Additionally, we discuss the
CrewAI implementation framework and present examples related to process mining.