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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
From Internet of Things Data to Business Processes: Challenges and a Framework
The IoT and Business Process Management (BPM) communities co-exist in many shared application domains, such as manufacturing and healthcare. The IoT community has a strong focus on hardware, connectivity and data; the BPM community focuses mainly on finding, controlling, and enhancing the structured interactions among the IoT devices in processes. While the field of Process Mining deals with the extraction of process models and process analytics from process event logs, the data produced by IoT sensors often is at a lower granularity than these process-level events. The fundamental questions about extracting and abstracting process-related data from streams of IoT sensor values are: (1) Which sensor values can be clustered together as part of process events?, (2) Which sensor values signify the start and end of such events?, (3) Which sensor values are related but not essential? This work proposes a framework to semi-automatically perform a set of structured steps to convert low-level IoT sensor data into higher-level process events that are suitable for process mining. The framework is meant to provide a generic sequence of abstract steps to guide the event extraction, abstraction, and correlation, with variation points for plugging in specific analysis techniques and algorithms for each step. To assess the completeness of the framework, we present a set of challenges, how they can be tackled through the framework, and an example on how to instantiate the framework in a real-world demonstration from the field of smart manufacturing. Based on this framework, future research can be conducted in a structured manner through refining and improving individual steps.
2024
A Systematic Review of Business Process Improvement: Achievements and Potentials in Combining Concepts from Operations Research and Business Process Management
Business Process Management and Operations Research are two research fields that both aim to enhance value creation in organizations. While Business Process Management has historically emphasized on providing precise models, Operations Research has focused on constructing tractable models and their solutions. This systematic literature review identifies and analyzes work that uses combined concepts from both disciplines. In particular, it analyzes how business process models have been conceptualized as mathematical models and which optimization techniques have been applied to these models. Results indicate a strong focus on resource allocation and scheduling problems. Current approaches often lack support of the stochastic nature of many problems, and do only sparsely use information from process models or from event logs, such as resource-related information or information from the data perspective.
2024
DigiEMine: Towards Leveraging Decision Mining and Context Data for Quality Control
Enterprise Design, Operations and Computing
2024