IPMD: Intentional Process Model Discovery from Event Logs

被引:0
|
作者
Elali, Ramona [1 ]
Kornyshova, Elena [2 ]
Deneckere, Rebecca [1 ]
Salinesi, Camille [1 ]
机构
[1] Paris 1 Pantheon Sorbonne, Paris, France
[2] Conservatoire Natl Arts & Metiers, Paris, France
关键词
Intention Mining; Intentional Process Model; Frequent Pattern Mining; Process Mining; Large Language Model;
D O I
10.1007/978-3-031-59468-7_5
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Intention Mining is a crucial aspect of understanding human behavior. It focuses on uncovering the underlying hidden intentions and goals that guide individuals in their activities. We propose the approach IPMD (Intentional Process Model Discovery) that combines Frequent Pattern Mining, Large Language Model, and Process Mining to construct intentional process models that capture the human strategies inherited from his decision-making and activity execution. This combination aims to identify recurrent sequences of actions revealing the strategies (recurring patterns of activities), that users commonly apply to fulfill their intentions. These patterns are used to construct an intentional process model that follows the MAP formalism based on strategy discovery.
引用
收藏
页码:38 / 46
页数:9
相关论文
共 50 条
  • [21] The impact of biased sampling of event logs on the performance of process discovery
    Fani Sani, Mohammadreza
    van Zelst, Sebastiaan J.
    van der Aalst, Wil M. P.
    COMPUTING, 2021, 103 (06) : 1085 - 1104
  • [22] Utility-Based Control Flow Discovery from Business Process Event Logs
    Anand, Kritika
    Gupta, Nisha
    Sureka, Ashish
    BIG DATA ANALYTICS, BDA 2015, 2015, 9498 : 69 - 83
  • [23] Discovering Process Model from Event Logs by Considering Overlapping Rules
    Effendi, Yutika Amelia
    Sarno, Riyanarto
    2017 4TH INTERNATIONAL CONFERENCE ON ELECTRICAL ENGINEERING, COMPUTER SCIENCE AND INFORMATICS (EECSI), 2017, : 645 - 650
  • [24] Process Model Discovery from Sensor Event Data
    Janssen, Dominik
    Mannhardt, Felix
    Koschmider, Agnes
    van Zelst, Sebastiaan J.
    PROCESS MINING WORKSHOPS, ICPM 2020 INTERNATIONAL WORKSHOPS, 2021, 406 : 69 - 81
  • [25] Discovering Declarative Process Model Behavior from Event Logs via Model Learning
    Agostinelli, Simone
    Bergami, Giacomo
    Fiorenza, Alessio
    Maggi, Fabrizio M.
    Marrella, Andrea
    Patrizi, Fabio
    2021 3RD INTERNATIONAL CONFERENCE ON PROCESS MINING (ICPM 2021), 2021, : 48 - 55
  • [26] Data-Driven Process Discovery - Revealing Conditional Infrequent Behavior from Event Logs
    Mannhardt, Felix
    de Leoni, Massimiliano
    Reijers, Hajo A.
    van der Aalst, Wil M. P.
    ADVANCED INFORMATION SYSTEMS ENGINEERING (CAISE 2017), 2017, 10253 : 545 - 560
  • [27] Split miner: automated discovery of accurate and simple business process models from event logs
    Adriano Augusto
    Raffaele Conforti
    Marlon Dumas
    Marcello La Rosa
    Artem Polyvyanyy
    Knowledge and Information Systems, 2019, 59 : 251 - 284
  • [28] Mining Process Performance from Event Logs
    Adriansyah, Arya
    Buijs, Joos C. A. M.
    BUSINESS PROCESS MANAGEMENT WORKSHOPS (BPM), 2013, 132 : 217 - 218
  • [29] Automated discovery of structured process models from event logs: The discover-and-structure approach
    Augusto, Adriano
    Conforti, Raffaele
    Dumas, Marlon
    La Rosa, Marcello
    Bruno, Giorgio
    DATA & KNOWLEDGE ENGINEERING, 2018, 117 : 373 - 392
  • [30] Split miner: automated discovery of accurate and simple business process models from event logs
    Augusto, Adriano
    Conforti, Raffaele
    Dumas, Marlon
    La Rosa, Marcello
    Polyvyanyy, Artem
    KNOWLEDGE AND INFORMATION SYSTEMS, 2019, 59 (02) : 251 - 284