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Recent Advances in Data-Driven Business Process Management

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arxiv 2406.01786 v1 pith:DZHGWC4M submitted 2024-06-03 cs.DB cs.AI

Lars Ackermann , Martin Käppel , Laura Marcus , Linda Moder , Sebastian Dunzer , Markus Hornsteiner , Annina Liessmann , Yorck Zisgen
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Philip Empl Lukas-Valentin Herm Nicolas Neis Julian Neuberger Leo Poss Myriam Schaschek Sven Weinzierl Niklas Wördehoff Stefan Jablonski Agnes Koschmider Wolfgang Kratsch Martin Matzner Stefanie Rinderle-Ma Maximilian Röglinger Stefan Schönig Axel Winkelmann
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classification cs.DBcs.AI
keywords businessdata-drivenmanagementresearchdataprocessadvancesarea
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The rapid development of cutting-edge technologies, the increasing volume of data and also the availability and processability of new types of data sources has led to a paradigm shift in data-based management and decision-making. Since business processes are at the core of organizational work, these developments heavily impact BPM as a crucial success factor for organizations. In view of this emerging potential, data-driven business process management has become a relevant and vibrant research area. Given the complexity and interdisciplinarity of the research field, this position paper therefore presents research insights regarding data-driven BPM.

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Cited by 1 Pith paper

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  1. FairLoop: Software Support for Human-Centric Fairness in Predictive Business Process Monitoring

    cs.LG 2025-08 conditional novelty 4.0 of 10

    FairLoop distills predictive neural models into editable decision trees, lets users cut out unfair rules, and fine-tunes the model on corrected labels for business process monitoring.

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