Recent Advances in Data-Driven Business Process Management
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arXiv
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| Autori principali: | , , , , , , , , , , , , , , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866929371830288384 |
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| author | Ackermann, Lars Käppel, Martin Marcus, Laura Moder, Linda Dunzer, Sebastian Hornsteiner, Markus Liessmann, Annina Zisgen, Yorck Empl, Philip Herm, Lukas-Valentin Neis, Nicolas Neuberger, Julian Poss, Leo Schaschek, Myriam Weinzierl, Sven Wördehoff, Niklas Jablonski, Stefan Koschmider, Agnes Kratsch, Wolfgang Matzner, Martin Rinderle-Ma, Stefanie Röglinger, Maximilian Schönig, Stefan Winkelmann, Axel |
| author_facet | Ackermann, Lars Käppel, Martin Marcus, Laura Moder, Linda Dunzer, Sebastian Hornsteiner, Markus Liessmann, Annina Zisgen, Yorck Empl, Philip Herm, Lukas-Valentin Neis, Nicolas Neuberger, Julian Poss, Leo Schaschek, Myriam Weinzierl, Sven Wördehoff, Niklas Jablonski, Stefan Koschmider, Agnes Kratsch, Wolfgang Matzner, Martin Rinderle-Ma, Stefanie Röglinger, Maximilian Schönig, Stefan Winkelmann, Axel |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_01786 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Recent Advances in Data-Driven Business Process Management Ackermann, Lars Käppel, Martin Marcus, Laura Moder, Linda Dunzer, Sebastian Hornsteiner, Markus Liessmann, Annina Zisgen, Yorck Empl, Philip Herm, Lukas-Valentin Neis, Nicolas Neuberger, Julian Poss, Leo Schaschek, Myriam Weinzierl, Sven Wördehoff, Niklas Jablonski, Stefan Koschmider, Agnes Kratsch, Wolfgang Matzner, Martin Rinderle-Ma, Stefanie Röglinger, Maximilian Schönig, Stefan Winkelmann, Axel Databases Artificial Intelligence 68U35 68T07 68T07, 68U35, 68T01 H.4.1; I.2.1; I.2.6; I.2.7; H.2.8; K.6.1 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. |
| title | Recent Advances in Data-Driven Business Process Management |
| topic | Databases Artificial Intelligence 68U35 68T07 68T07, 68U35, 68T01 H.4.1; I.2.1; I.2.6; I.2.7; H.2.8; K.6.1 |
| url | https://arxiv.org/abs/2406.01786 |