Business Process Event Log use for Activity Sequence Analysis

被引:0
|
作者
Savickas, Titas [1 ]
Vasilecas, Olegas [1 ]
机构
[1] Vilnius Gediminas Tech Univ, Informat Syst Lab, Vilnius, Lithuania
来源
2015 OPEN CONFERENCE OF ELECTRICAL, ELECTRONIC AND INFORMATION SCIENCES (ESTREAM) | 2015年
关键词
process mining; event log; activity sequence; business process analysis; frequency matrix;
D O I
暂无
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
It is mandatory for nowadays businesses to improve their processes to survive. The basis of this improvement is business process analysis. The analysis can be done in multiple ways but one of the state of the art solutions is Process Mining which uses historical business process execution data in information systems to improve the process analysis. The historical data for analysis comes in a form of event logs. In this paper an approach is presented which processes an event log into frequency matrixes to allow efficient activity sequence analysis. Additionally, some heuristic rules are presented that allow to make inference on causality between activities in a business process.
引用
收藏
页数:4
相关论文
共 50 条
  • [1] An approach for analyzing business process execution complexity based on textual data and event log
    Revina, Aleksandra
    Aksu, Unal
    INFORMATION SYSTEMS, 2023, 114
  • [2] Belief network discovery from event logs for business process analysis
    Savickas, Titas
    Vasilecas, Olegas
    COMPUTERS IN INDUSTRY, 2018, 100 : 258 - 266
  • [3] Business process discovery as a service with event log privacy and access control over discovered models
    de la Fuente-anaya, Hector A.
    Marin-Castro, Heidy M.
    Morales-Sandoval, Miguel
    Garcia-Hernandez, Jose Juan
    COMPUTING, 2024, 106 (11) : 3603 - 3625
  • [4] Event Log Preprocessing for Process Mining: A Review
    Marin-Castro, Heidy M.
    Tello-Leal, Edgar
    APPLIED SCIENCES-BASEL, 2021, 11 (22):
  • [5] Decision Support Using Belief Network Constructed from Business Process Event Log
    Savickas, Titas
    Vasilecas, Olegas
    INFORMATICA, 2017, 28 (04) : 687 - 701
  • [6] Extraction of Missing Tendency Using Decision Tree Learning in Business Process Event Log
    Horita, Hiroki
    Kurihashi, Yuta
    Miyamori, Nozomi
    DATA, 2020, 5 (03) : 1 - 12
  • [7] Time and activity sequence prediction of business process instances
    Polato, Mirko
    Sperduti, Alessandro
    Burattin, Andrea
    de Leoni, Massimiliano
    COMPUTING, 2018, 100 (09) : 1005 - 1031
  • [8] Time and activity sequence prediction of business process instances
    Mirko Polato
    Alessandro Sperduti
    Andrea Burattin
    Massimiliano de Leoni
    Computing, 2018, 100 : 1005 - 1031
  • [9] Building a valuable event log for process mining: an experimental exploration of a guided process
    Jans, Mieke
    Soffer, Pnina
    Jouck, Toon
    ENTERPRISE INFORMATION SYSTEMS, 2019, 13 (05) : 601 - 630
  • [10] Anomaly detection algorithm for business process control flow based on event log: Status and evaluation
    Fu, Jianping
    Zhao, Haiyan
    Cao, Jian
    Chen, Qingkui
    Jisuanji Jicheng Zhizao Xitong/Computer Integrated Manufacturing Systems, CIMS, 2024, 30 (08): : 2631 - 2643