Auflistung nach Autor:in "Evermann, Joerg"
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- ZeitschriftenartikelA Novel Business Process Prediction Model Using a Deep Learning Method(Business & Information Systems Engineering: Vol. 62, No. 2, 2020) Mehdiyev, Nijat; Evermann, Joerg; Fettke, PeterThe ability to proactively monitor business processes is a main competitive differentiator for firms. Process execution logs generated by process aware information systems help to make process specific predictions for enabling a proactive situational awareness. The goal of the proposed approach is to predict the next process event from the completed activities of the running process instance, based on the execution log data from previously completed process instances. By predicting process events, companies can initiate timely interventions to address undesired deviations from the desired workflow. The paper proposes a multi-stage deep learning approach that formulates the next event prediction problem as a classification problem. Following a feature pre-processing stage with n-grams and feature hashing, a deep learning model consisting of an unsupervised pre-training component with stacked autoencoders and a supervised fine-tuning component is applied. Experiments on a variety of business process log datasets show that the multi-stage deep learning approach provides promising results. The study also compared the results to existing deep recurrent neural networks and conventional classification approaches. Furthermore, the paper addresses the identification of suitable hyperparameters for the proposed approach, and the handling of the imbalanced nature of business process event datasets.
- ZeitschriftenartikelCall for Papers, Issue 3/2025(Business & Information Systems Engineering: Vol. 65, No. 5, 2023) Río Ortega, Adela; Beerepoot, Iris; Aa, Han; Evermann, Joerg
- ZeitschriftenartikelWorkflow Management on BFT Blockchains(Enterprise Modelling and Information Systems Architectures (EMISAJ) – International Journal of Conceptual Modeling: Vol. 15, Nr. 14, 2020) Evermann, Joerg; Kim, HenryBlockchains have been proposed as infrastructure technology for a wide variety of applications. They provide an immutable record of transactions, making them useful when business actors do not trust each other, and their distributed nature makes them suitable for inter-organizational applications. However, widely-used proof-of-work based blockchains are computationally inefficient and do not provide final consensus, although they scale well to large networks. In contrast, blockchains built around Byzantine Fault Tolerance (BFT) consensus algorithms are more efficient and provide immediate and final consensus, but do not scale well to large networks. We argue that this makes them well-suited for workflow management applications, which typically include no more than a few dozen participants. This paper is motivated by a use case in the resource extraction industry. We develop an architecture for a BFT blockchain based workflow management system (WfMS) and present a prototype implementation. We discuss its advantages and limitations with respect to proof-of-work based systems and provide an outlook to future research.