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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
July
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
NL2ProcessOps: Towards LLM-guided Code Generation for Process Execution
Business Process Management Forum
2024
Interactive Drift Visualization in Sensor Data Streams for Explainable Process Outcome Prediction
2024
Towards a Comprehensive Evaluation of Decision Rules and Decision Mining Algorithms Beyond Accuracy
Advanced Information Systems Engineering
Springer Nature Switzerland
2024
Automatic Extraction and Formalization of Temporal Requirements from Text: A Survey
Enterprise Design, Operations, and Computing
2024