Redescription mining-based business process deviance analysisRedescription mining-based business process deviance analysisE. Ahmeti et al.

被引:0
|
作者
Engjëll Ahmeti [1 ]
Martin Käppel [2 ]
Stefan Jablonski [2 ]
机构
[1] Fraunhofer Institute for Manufacturing Engineering and Automation IPA Bayreuth,Institute for Computer Science
[2] University of Bayreuth,undefined
关键词
Deviance mining; Redescription mining; Process mining; Natural language generation;
D O I
10.1007/s10270-024-01231-8
中图分类号
学科分类号
摘要
Business processes often deviate from their expected or desired behavior. Such deviations can be either positive or negative, depending on whether or not they lead to better process performance. Deviance mining addresses the problem of identifying such deviations and explaining why a process deviates. In this paper, we propose a novel approach to identify and explain the causes of deviant process executions based on the technique of redescription mining, which extracts knowledge in the form of logical rules. By analyzing, comparing, and filtering these rules, the reasons for the deviant behaviors of a business process are identified both in general and for particular process instances. Afterward, the results of this analysis are transformed into a concise and well-readable natural language text that can be used by business analysts and process owners to optimize processes in a reasoned manner. We evaluate our approach from different angles using four process models and provide some advice for further optimization.
引用
收藏
页码:1421 / 1450
页数:29
相关论文
共 43 条
  • [21] A policy-based process mining framework: mining business policy texts for discovering process models
    Jiexun Li
    Harry Jiannan Wang
    Zhu Zhang
    J. Leon Zhao
    Information Systems and e-Business Management, 2010, 8 : 169 - 188
  • [22] The Mining of Activity Dependence Relation based on Business Process Models
    Hu, Guangchang
    Wu, Budan
    Chen, Junliang
    2017 IEEE INTERNATIONAL CONFERENCE ON SERVICES COMPUTING (SCC), 2017, : 450 - 458
  • [23] Anomaly Detection in Business Process based on Data Stream Mining
    Tavares, Gabriel Marques
    Turrisi da Costa, Victor G.
    Martins, Vinicius Eiji
    Ceravolo, Paolo
    Barbon, Sylvio, Jr.
    PROCEEDINGS OF THE 14TH BRAZILIAN SYMPOSIUM ON INFORMATION SYSTEMS (SBSI2018), 2018, : 120 - 127
  • [24] A New Method for Business Process Mining Based on State Equation
    Hu, Hua
    Xie, Jianen
    Hu, Haiyang
    WEB INFORMATION SYSTEMS ENGINEERING - WISE 2010 WORKSHOPS, 2011, 6724 : 474 - 482
  • [25] Business Process Analysis in Advertising: an Extension to a Methodology Based on Process Mining Projects
    Osses, Anbal Silva
    Arias, Michael
    Da Silva, Luiz Quelves
    Rojas, Eric
    Cobo, Bernardita Fernandez
    Munoz-Gama, Jorge
    Fernadez, Marcos Seplveda
    PROCEEDINGS OF THE 2016 35TH INTERNATIONAL CONFERENCE OF THE CHILEAN COMPUTER SCIENCE SOCIETY (SCCC), 2016,
  • [26] A Business Process Analysis Methodology Based on Process Mining for Complaint Handling Service Processes
    Wu, Qiong
    He, Zhen
    Wang, Haijie
    Wen, Lijie
    Yu, Tongzhou
    APPLIED SCIENCES-BASEL, 2019, 9 (16):
  • [27] Redesigning business processes: a methodology based on simulation and process mining techniques
    Maruster, Laura
    van Beest, Nick R. T. P.
    KNOWLEDGE AND INFORMATION SYSTEMS, 2009, 21 (03) : 267 - 297
  • [28] Hybrid Cuckoo Search-Based Algorithms for Business Process Mining
    Chifu, Viorica R.
    Pop, Cristina Bianca
    Salomie, Ioan
    Chifu, Emil St.
    Rad, Victor
    Antal, Marcel
    INTELLIGENT SYSTEMS'2014, VOL 1: MATHEMATICAL FOUNDATIONS, THEORY, ANALYSES, 2015, 322 : 487 - 498
  • [29] Rule-based control of business processes - A process mining approach
    Grob, Heinz Lothar
    Bensberg, Frank
    Coners, Andre
    WIRTSCHAFTSINFORMATIK, 2008, 50 (04): : 268 - 281
  • [30] Scalable Attack Analysis of Business Process based on Decision Mining Classification
    Rahmawati, Dewi
    Sarno, Riyanarto
    2017 4TH INTERNATIONAL CONFERENCE ON ELECTRICAL ENGINEERING, COMPUTER SCIENCE AND INFORMATICS (EECSI), 2017, : 337 - 342