Discovering instance and process spanning constraints from process execution logs

被引:17
作者
Winter, Karolin [1 ]
Stertz, Florian [1 ]
Rinderle-Ma, Stefanie [1 ,2 ]
机构
[1] Univ Vienna, Fac Comp Sci, Res Grp Workflow Syst & Technol, Vienna, Austria
[2] Univ Vienna, Data Sci Uni Vienna, Vienna, Austria
关键词
Digitalized compliance management; Constraint mining; Instance spanning constraints; Process mining; CONFORMANCE CHECKING;
D O I
10.1016/j.is.2019.101484
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Instance spanning constraints (ISC) are the instrument to establish controls across multiple instances of one or several processes. A multitude of applications crave for ISC support. Consider, for example, the bundling and unbundling of cargo across several instances of a logistics process or dependencies between examinations in different medical treatment processes. Non-compliance with ISC can lead to severe consequences and penalties, e.g., dangerous effects due to undesired drug interactions. ISC might stem from regulatory documents, extracted by domain experts. Another source for ISC are process execution logs. Process execution logs store execution information for process instances, and hence, inherently, the effects of ISC. Discovering ISC from process execution logs can support ISC design and implementation (if the ISC was not known beforehand) and the validation of the ISC during its life time. This work contributes a categorization of ISC as well as four discovery algorithms for ISC candidates from process execution logs. The discovered ISC candidates are put into context of the associated processes and can be further validated with domain experts. The algorithms are prototypically implemented and evaluated based on artificial and real-world process execution logs. The results facilitate ISC design as well as validation and hence contribute to a digitalized ISC and compliance management. (C) 2019 The Authors. Published by Elsevier Ltd.
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页数:20
相关论文
共 55 条
  • [1] Agrawal R., 1993, SIGMOD Record, V22, P207, DOI 10.1145/170036.170072
  • [2] AGRAWAL R, 1995, PROC INT CONF DATA, P3, DOI 10.1109/ICDE.1995.380415
  • [3] MAINTAINING KNOWLEDGE ABOUT TEMPORAL INTERVALS
    ALLEN, JF
    [J]. COMMUNICATIONS OF THE ACM, 1983, 26 (11) : 832 - 843
  • [4] Improving pathway compliance and clinician performance by using information technology
    Blaser, R.
    Schnabel, M.
    Biber, C.
    Baeumlein, M.
    Heger, O.
    Beyer, M.
    Opitz, E.
    Lenz, R.
    Kuhn, K. A.
    [J]. INTERNATIONAL JOURNAL OF MEDICAL INFORMATICS, 2007, 76 (2-3) : 151 - 156
  • [5] Association Rules for Anomaly Detection and Root Cause Analysis in Process Executions
    Boehmer, Kristof
    Rinderle-Ma, Stefanie
    [J]. ADVANCED INFORMATION SYSTEMS ENGINEERING, CAISE 2018, 2018, 10816 : 3 - 18
  • [6] Bose R. P. Jagadeesh Chandra, 2011, Advanced Information Systems Engineering. Proceedings 23rd International Conference, CAiSE 2011, P391, DOI 10.1007/978-3-642-21640-4_30
  • [7] Online Conformance Checking Using Behavioural Patterns
    Burattin, Andrea
    van Zelst, Sebastiaan J.
    Armas-Cervantes, Abel
    van Dongen, Boudewijn F.
    Carmona, Josep
    [J]. BUSINESS PROCESS MANAGEMENT (BPM 2018), 2018, 11080 : 250 - 267
  • [8] Online Discovery of Declarative Process Models from Event Streams
    Burattin, Andrea
    Cimitile, Marta
    Maggi, Fabrizio M.
    Sperduti, Alessandro
    [J]. IEEE TRANSACTIONS ON SERVICES COMPUTING, 2015, 8 (06) : 833 - 846
  • [9] 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
  • [10] Identifying refactoring opportunities in process model repositories
    Dijkman, Remco
    Gfeller, Beat
    Kuester, Jochen
    Voelzer, Hagen
    [J]. INFORMATION AND SOFTWARE TECHNOLOGY, 2011, 53 (09) : 937 - 948