Discovering work prioritisation patterns from event logs

被引:15
|
作者
Suriadi, Suriadi [1 ]
Wynn, Moe T. [2 ]
Xu, Jingxin [1 ]
van der Aalst, Wil M. P. [1 ,4 ]
ter Hofstede, Arthur H. M. [3 ,5 ]
机构
[1] Queensland Univ Technol, Brisbane, Qld, Australia
[2] Queensland Univ Technol, BPM, Sch Informat Syst, Brisbane, Qld, Australia
[3] Queensland Univ Technol, Informat Syst Sch, Sci & Engn Fac, Brisbane, Qld, Australia
[4] Eindhoven Univ Technol, Informat Syst, Eindhoven, Netherlands
[5] Eindhoven Univ Technol, Informat Syst Grp, Dept Ind Engn, Eindhoven, Netherlands
关键词
Resource behaviour mining; Queuing; Process mining; TIME; PERFORMANCE; DISCIPLINE; FAIRNESS; SYSTEMS; QUEUES;
D O I
10.1016/j.dss.2017.02.002
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Business process improvement initiatives typically employ various process analysis techniques, including evidence-based analysis techniques such as process mining, to identify new ways to streamline current business processes. While plenty of process mining techniques have been proposed to extract insights about the way in which activities within processes are conducted, techniques to understand resource behaviour are limited. At the same time, an understanding of resources behaviour is critical to enable intelligent and effective resource management - an important factor which can significantly impact overall process performance. The presence of detailed records kept by today's organisations, including data about who, how, what, and when various activities were carried out by resources, open up the possibility for real behaviours of resources to be studied. This paper proposes an approach to analyse one aspect of resource behaviour: the manner in which a resource prioritises his/her work. The proposed approach has been formalised, implemented, and evaluated using a number of synthetic and real datasets. (C) 2017 Elsevier B.V. All rights reserved.
引用
收藏
页码:77 / 92
页数:16
相关论文
共 50 条
  • [1] Discovering Signature Patterns from Event Logs
    Bose, R. P. Jagadeesh Chandra
    van der Aalst, Wil M. P.
    [J]. 2013 IEEE SYMPOSIUM ON COMPUTATIONAL INTELLIGENCE AND DATA MINING (CIDM), 2013, : 111 - 118
  • [2] Discovering and Analyzing Contextual Behavioral Patterns From Event Logs
    Acheli, Mehdi
    Grigori, Daniela
    Weidlich, Matthias
    [J]. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, 2022, 34 (12) : 5708 - 5721
  • [3] Discovering anomalous frequent patterns from partially ordered event logs
    Genga, Laura
    Alizadeh, Mahdi
    Potena, Domenico
    Diamantini, Claudia
    Zannone, Nicola
    [J]. JOURNAL OF INTELLIGENT INFORMATION SYSTEMS, 2018, 51 (02) : 257 - 300
  • [4] Discovering anomalous frequent patterns from partially ordered event logs
    Laura Genga
    Mahdi Alizadeh
    Domenico Potena
    Claudia Diamantini
    Nicola Zannone
    [J]. Journal of Intelligent Information Systems, 2018, 51 : 257 - 300
  • [5] On Systematic Approach to Discovering Periodic Patterns in Event Logs
    Zimniak, Marcin
    Getta, Janusz R.
    [J]. COMPUTATIONAL COLLECTIVE INTELLIGENCE, ICCCI 2016, PT I, 2016, 9875 : 249 - 259
  • [6] Discovering Decision Models from Event Logs
    Bazhenova, Ekaterina
    Buelow, Susanne
    Weske, Mathias
    [J]. BUSINESS INFORMATION SYSTEMS (BIS 2016), 2016, 255 : 237 - 251
  • [7] Discovering Data Models from Event Logs
    Bano, Dorina
    Weske, Mathias
    [J]. CONCEPTUAL MODELING, ER 2020, 2020, 12400 : 62 - 76
  • [8] Discovering social networks from event logs
    Van Der Aalst W.M.P.
    Reijers H.A.
    Song M.
    [J]. Computer Supported Cooperative Work (CSCW), 2005, 14 (6): : 549 - 593
  • [9] Discovering Unseen Behaviour from Event Logs
    Cervantes, Abel Armas
    Taymouri, Farbod
    [J]. APPLICATION AND THEORY OF PETRI NETS AND CONCURRENCY (PETRI NETS 2022), 2022, 13288 : 23 - 42
  • [10] An Experimental Analytics on Discovering Work Transference Networks from Workflow Enactment Event Logs
    Hyun Ahn
    Dinh-Lam Pham
    Kim, Kwanghoon Pio
    [J]. APPLIED SCIENCES-BASEL, 2019, 9 (11):