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Analyzing The Use of Ethical Theories Within AI Ethics Research: A Systematic Scoping Review
Wirtschaftsinformatik 2024
Würzburg, Deutschland
2024
Prototyping a mobile app which detects dogs’ emotions based on their body posture: a design science approach
Handbook of Social Computing
Edward Elgar Publishing
2024
Collaboration Miner: Discovering Collaboration Petri Nets (Extended Version)
Most existing process discovery techniques aim to mine models of process orchestrations that represent behavior of cases within one business process. Collaboration process discovery techniques mine models of collaboration processes that represent behavior of collaborating cases within multiple process orchestrations that interact via collaboration concepts such as organizations, agents, and services. While workflow nets are mostly mined for process orchestrations, a standard model for collaboration processes is missing. Hence, in this work, we rely on the newly proposed collaboration Petri nets and show that in combination with the newly proposed Collaboration Miner (CM), the resulting representational bias is lower than for existing models. Moreover, CM can discover heterogeneous collaboration concepts and types such as resource sharing and message exchange, resulting in fitting and precise collaboration Petri nets. The evaluation shows that CM achieves its design goals: no assumptions on concepts and types as well as fitting and precise models, based on 26 artificial and real-world event logs from literature.
2024
INEXA: Interactive and Explainable Process Model Abstraction Through Object-Centric Process Mining
Process events are recorded by multiple information systems at different granularity levels. Based on the resulting event logs, process models are discovered at different granularity levels, as well. Events stored at a fine-grained granularity level, for example, may hinder the discovered process model to be displayed due the high number of resulting model elements. The discovered process model of a real-world manufacturing process, for example, consists of 1,489 model elements and over 2,000 arcs. Existing process model abstraction techniques could help reducing the size of the model, but would disconnect it from the underlying event log. Existing event abstraction techniques do neither support the analysis of mixed granularity levels, nor interactive exploration of a suitable granularity level. To enable the exploration of discovered process models at different granularity levels, we propose INEXA, an interactive, explainable process model abstraction method that keeps the link to the event log. As a starting point, INEXA aggregates large process models to a "displayable" size, e.g., for the manufacturing use case to a process model with 58 model elements. Then, the process analyst can explore granularity levels interactively, while applied abstractions are automatically traced in the event log for explainability.
2024
Design of a Quality Management System Based on the EU AI Act
Frontiers in Artificial Intelligence and Applications
Savelka, Jaromir;Harasta, Jakub;Novotna, Tereza;Misek, Jakub
IOS Press
2024
A quantitative questionnaire for SAP-based data analytics in education
SAP Academic Community Conference 2024 (D-A-CH)
Wien, Österreich
2024
Online Resource Allocation to Process Tasks Under Uncertain Resource Availabilities
137-144
2024 6th International Conference on Process Mining (ICPM)
IEEE
2024
BPMS Blockchain Technology Soft Integration For Non-tamperable Logging
106--120
Business Process Management: Blockchain, Robotic Process Automation, Central and Eastern European, Educators and Industry Forum
Di Ciccio, Claudio;Fdhila, Walid;Agostinelli, Simone;Amyot, Daniel;Leopold, Henrik;Krčál, Michal;Malinova Mandelburger, Monika;Polančič, Gregor;Tomičić-Pupek, Katarina;Gdowska, Katarzyna;Grisold, Thomas;Sliż, Piotr;Beerepoot, Iris;Gabryelczyk, Renata;Plattfaut, Ralf
Springer Nature Switzerland
2024
Large Process Models: A Vision for Business Process Management in the Age of Generative AI
KI - Künstliche Intelligenz
2024