RuM: Declarative Process Mining, Distilled

被引:7
|
作者
Alman, Anti [1 ]
Di Ciccio, Claudio [2 ]
Maggi, Fabrizio Maria [3 ]
Montali, Marco [3 ]
van der Aa, Han [4 ]
机构
[1] Univ Tartu, Tartu, Estonia
[2] Sapienza Univ Rome, Rome, Italy
[3] Free Univ Bozen Bolzano, Bolzano, Italy
[4] Univ Mannheim, Mannheim, Germany
来源
关键词
Declare; Declarative process mining; Rule mining; Process discovery; Conformance checking; Process monitoring; Declarative modelling; MODELS;
D O I
10.1007/978-3-030-85469-0_3
中图分类号
F [经济];
学科分类号
02 ;
摘要
Flexibility is a key characteristic of numerous business process management domains. In these domains, the paths to fulfil process goals may not be fully predetermined, but can strongly depend on dynamic decisions made based on the current circumstances of a case. A common example is the adaptation of a standard treatment process to the needs of a specific patient. However, high flexibility does not mean chaos: certain key process rules still delimit the execution space, such as rules that prohibit the joint administration of certain drugs in a treatment, due to dangerous interactions. A renowned means to handle flexibility by design is the declarative approach, which aims to define processes through their core behavioural rules, thus leaving room for dynamic adaptation. This declarative approach to both process modelling and mining involves a paradigm shift in process thinking and, therefore, the support of novel concepts and tools. Complementing our tutorial with the same title, this paper provides a high-level introduction to declarative process mining, including its operationalisation through the RuM toolkit, key conceptual considerations, and an outlook for the future.
引用
收藏
页码:23 / 29
页数:7
相关论文
共 50 条
  • [41] Declarative Process Discovery: Linking Process and Textual Views
    Lopez, Hugo A.
    Stromsted, Rasmus
    Niyodusenga, Jean-Marie
    Marquard, Morten
    [J]. INTELLIGENT INFORMATION SYSTEMS, CAISE FORUM 2021, 2021, 424 : 109 - 117
  • [42] Declarative data mining using SQL3
    Jamil, HM
    [J]. DATABASE SUPPORT FOR DATA MINING APPLICATIONS: DISCOVERING KNOWLEDGE WITH INDUCTIVE QUERIES, 2004, 2682 : 52 - 75
  • [43] A declarative framework for work process configuration
    Mayer, Wolfgang
    Stumptner, Markus
    Killisperger, Peter
    Grossmann, Georg
    [J]. AI EDAM-ARTIFICIAL INTELLIGENCE FOR ENGINEERING DESIGN ANALYSIS AND MANUFACTURING, 2011, 25 (02): : 143 - 162
  • [44] Measuring Inconsistency in Declarative Process Specifications
    Corea, Carl
    Grant, John
    Thimm, Matthias
    [J]. BUSINESS PROCESS MANAGEMENT (BPM 2022), 2022, 13420 : 289 - 306
  • [45] Declarative Aspects in Explicative Data Mining for Computational Sensemaking
    Atzmueller, Martin
    [J]. DECLARATIVE PROGRAMMING AND KNOWLEDGE MANAGEMENT, DECLARE 2017, 2018, 10997 : 97 - 114
  • [46] The Analysis of a Real Life Declarative Process
    Debois, Soren
    Slaats, Tijs
    [J]. 2015 IEEE SYMPOSIUM SERIES ON COMPUTATIONAL INTELLIGENCE (IEEE SSCI), 2015, : 1374 - 1382
  • [47] Declarative Process Discovery with Evolutionary Computing
    vanden Broucke, Seppe K. L. M.
    Vanthienen, Jan
    Baesens, Bart
    [J]. 2014 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC), 2014, : 2412 - 2419
  • [48] Anonymous class in declarative process modeling
    Allan, BA
    Westerberg, AW
    [J]. INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH, 1999, 38 (03) : 692 - 704
  • [49] Integrated Declarative Process and Decision Discovery of the Emergency Care Process
    Mertens, Steven
    Gailly, Frederik
    Van Sassenbroeck, Diederik
    Poels, Geert
    [J]. INFORMATION SYSTEMS FRONTIERS, 2022, 24 (01) : 305 - 327
  • [50] Supporting business process variability through declarative process families
    Groefsema, H.
    van Beest, N. R. T. P.
    [J]. COMPUTERS IN INDUSTRY, 2024, 159