From action to response to effect
- Process mining techniques are valuable to gain insights into and help improve (work) processes. Many of these techniques focus on the sequential order in which activities are performed. Few of these techniques consider the statistical relations within processes. In particular, existing techniques do not allow insights into how responses to an event (action) result in desired or undesired outcomes (effects). We propose and formalize the ARE miner, a novel technique that allows us to analyze and understand these action-response-effect patterns. We take a statistical approach to uncover potential dependency relations in these patterns. The goal of this research is to generate processes that are: (1) appropriately represented, and (2) effectively filtered to show meaningful relations. We evaluate the ARE miner in two ways. First, we use an artificial data set to demonstrate the effectiveness of the ARE miner compared to two traditional process-oriented approaches. Second, we apply the ARE miner to a real-world data set from a DutchProcess mining techniques are valuable to gain insights into and help improve (work) processes. Many of these techniques focus on the sequential order in which activities are performed. Few of these techniques consider the statistical relations within processes. In particular, existing techniques do not allow insights into how responses to an event (action) result in desired or undesired outcomes (effects). We propose and formalize the ARE miner, a novel technique that allows us to analyze and understand these action-response-effect patterns. We take a statistical approach to uncover potential dependency relations in these patterns. The goal of this research is to generate processes that are: (1) appropriately represented, and (2) effectively filtered to show meaningful relations. We evaluate the ARE miner in two ways. First, we use an artificial data set to demonstrate the effectiveness of the ARE miner compared to two traditional process-oriented approaches. Second, we apply the ARE miner to a real-world data set from a Dutch healthcare institution. We show that the ARE miner generates comprehensible representations that lead to informative insights into statistical relations between actions, responses, and effects.…
Author details: | Jelmer Jan KoornORCiD, Xixi LuORCiD, Henrik LeopoldORCiDGND, Hajo A. ReijersORCiDGND |
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DOI: | https://doi.org/10.1016/j.is.2022.102035 |
ISSN: | 0306-4379 |
ISSN: | 0094-453X |
Title of parent work (English): | Information systems : IS ; an international journal ; data bases |
Subtitle (English): | mining statistical relations in work processes |
Publisher: | Elsevier |
Place of publishing: | Amsterdam |
Publication type: | Article |
Language: | English |
Date of first publication: | 2022/11/01 |
Publication year: | 2022 |
Release date: | 2024/02/29 |
Tag: | Effect measurement; Process discovery; Statistical process mining |
Volume: | 109 |
Article number: | 102035 |
Number of pages: | 14 |
Funding institution: | NWO TACTICS project [628.011.004]; Lunet Zorg in the Netherlands |
Organizational units: | Digital Engineering Fakultät / Hasso-Plattner-Institut für Digital Engineering GmbH |
DDC classification: | 0 Informatik, Informationswissenschaft, allgemeine Werke / 07 Publizistische Medien, Journalismus, Verlagswesen / 070 Publizistische Medien, Journalismus, Verlagswesen |
Peer review: | Referiert |
Publishing method: | Open Access / Hybrid Open-Access |
License (German): | CC-BY - Namensnennung 4.0 International |