Process and Organizational Data Integration from BPMS and Relational/NoSQL Sources for Process Mining

被引:2
作者
Delgado, Andrea [1 ]
Calegari, Daniel [1 ]
机构
[1] Univ Republica, Fac Ingn, Inst Comp, Montevideo 11300, Uruguay
来源
PROCEEDINGS OF THE 17TH INTERNATIONAL CONFERENCE ON SOFTWARE TECHNOLOGIES (ICSOFT) | 2022年
关键词
Process Mining; Data Science; Process and Organizational Data Integration; Process Improvement; FRAMEWORK;
D O I
10.5220/0011322500003266
中图分类号
TP31 [计算机软件];
学科分类号
081202 ; 0835 ;
摘要
Business Process execution analysis is crucial for organizations to evaluate and improve them. Process mining provides the means to do so, but several challenges arise when dealing with data extraction and integration. Most scenarios consider implicit processes in support systems, with the process and organizational data being analyzed separately. Nowadays, many organizations increasingly integrate process-oriented support systems, such as BPMS, where process data execution is registered within the process engine database and organizational data in distributed potentially heterogeneous databases. They can follow the relational model or NoSQL ones, and organizational data can come from different systems, services, social media, or several other sources. Then, process and organizational data must be integrated to be used as input for process mining tasks and provide a complete view of the operation to detect and make improvements. In this paper, we extend previous work to support the collection of process and organizational data from heterogeneous sources, the integration of these data, and the automated generation of XES event logs to be used as input for process mining.
引用
收藏
页码:557 / 566
页数:10
相关论文
共 17 条
  • [1] Berti A., 2020, ABS200102562 CORR
  • [2] Merging event logs for process mining: A rule based merging method and rule suggestion algorithm
    Claes, Jan
    Poels, Geert
    [J]. EXPERT SYSTEMS WITH APPLICATIONS, 2014, 41 (16) : 7291 - 7306
  • [3] Connecting databases with process mining: a meta model and toolset
    de Murillas, Eduardo Gonzalez Lopez
    Reijers, Hajo A.
    van der Aalst, Wil M. P.
    [J]. SOFTWARE AND SYSTEMS MODELING, 2019, 18 (02) : 1209 - 1247
  • [4] Towards a unified vision of business process and organizational data
    Delgado, Andrea
    Calegari, Daniel
    [J]. 2020 XLVI LATIN AMERICAN COMPUTING CONFERENCE (CLEI 2020), 2021, : 108 - 117
  • [5] Delgado A., 2021, MODEL DRIVEN MANAGEM, DOI [10.5281/zenodo.5768180, DOI 10.5281/ZENODO.5768180]
  • [6] A Methodology for Integrated Process and Data Mining and Analysis towards Evidence-based Process Improvement
    Delgado, Andrea
    Calegari, Daniel
    Marotta, Adriana
    Gonzalez, Laura
    Tansini, Libertad
    [J]. PROCEEDINGS OF THE 16TH INTERNATIONAL CONFERENCE ON SOFTWARE TECHNOLOGIES (ICSOFT), 2021, : 426 - 437
  • [7] Towards a Generic BPMS User Portal Definition for the Execution of Business Processes
    Delgado, Andrea
    Calegari, Daniel
    Arrigoni, Andres
    [J]. ELECTRONIC NOTES IN THEORETICAL COMPUTER SCIENCE, 2016, 329 : 39 - 59
  • [8] Dumas M, 2018, Fundamentals of Business Process Management, P31, DOI DOI 10.1007/978-3-642-33143-5.PDF
  • [9] Furht B., 2016, BIG DATA TECHNOLOGIE, P3, DOI [10.1007/978-3-319-44550-2_1, DOI 10.1007/978-3-319-44550-2_1]
  • [10] IEEE, 2020, TASK FORC DAT SCI AD