@incollection{ThimGrumSchueffleretal.2021,
  author    = {Thim, Christof and Grum, Marcus and Sch{\"u}ffler, Arnulf and Roling, Wiebke and Kluge, Annette and Gronau, Norbert},
  title     = {A concept for a distributed Interchangeable knowledge base in CPPS},
  series = {Towards sustainable customization: cridging smart products and manufacturing systems},
  booktitle = {Towards sustainable customization: cridging smart products and manufacturing systems},
  editor    = {Andersen, Ann-Louise and Andersen, Rasmus and Brunoe, Thomas Ditlev and Larsen, Maria Stoettrup Schioenning and Nielsen, Kjeld and Napoleone, Alessia and Kjeldgaard, Stefan},
  publisher = {Springer},
  address   = {Cham},
  isbn      = {978-3-030-90699-3},
  doi       = {10.1007/978-3-030-90700-6_35},
  pages     = {314 -- 321},
  year      = {2021},
  abstract  = {As AI technology is increasingly used in production systems, different approaches have emerged from highly decentralized small-scale AI at the edge level to centralized, cloud-based services used for higher-order optimizations. Each direction has disadvantages ranging from the lack of computational power at the edge level to the reliance on stable network connections with the centralized approach. Thus, a hybrid approach with centralized and decentralized components that possess specific abilities and interact is preferred. However, the distribution of AI capabilities leads to problems in self-adapting learning systems, as knowledgebases can diverge when no central coordination is present. Edge components will specialize in distinctive patterns (overlearn), which hampers their adaptability for different cases. Therefore, this paper aims to present a concept for a distributed interchangeable knowledge base in CPPS. The approach is based on various AI components and concepts for each participating node. A service-oriented infrastructure allows a decentralized, loosely coupled architecture of the CPPS. By exchanging knowledge bases between nodes, the overall system should become more adaptive, as each node can "forget" their present specialization.},
  language  = {en}
}
@incollection{ThimGronauHaaseetal.2023,
  author    = {Thim, Christof and Gronau, Norbert and Haase, Jennifer and Grum, Marcus and Sch{\"u}ffler, Arnulf and Roling, Wiebke and Kluge, Annette},
  title     = {Modeling change in business processes},
  series = {Business modeling and software design},
  booktitle = {Business modeling and software design},
  editor    = {Shishkov, Boris},
  publisher = {Springer Nature},
  address   = {Cham},
  isbn      = {978-3-031-36756-4},
  doi       = {10.1007/978-3-031-36757-1_1},
  pages     = {3 -- 17},
  year      = {2023},
  abstract  = {Business processes are regularly modified either to capture requirements from the organization's environment or due to internal optimization and restructuring. Implementing the changes into the individual work routines is aided by change management tools. These tools aim at the acceptance of the process by and empowerment of the process executor. They cover a wide range of general factors and seldom accurately address the changes in task execution and sequence. Furthermore, change is only framed as a learning activity, while most obstacles to change arise from the inability to unlearn or forget behavioural patterns one is acquainted with. Therefore, this paper aims to develop and demonstrate a notation to capture changes in business processes and identify elements that are likely to present obstacles during change. It connects existing research from changes in work routines and psychological insights from unlearning and intentional forgetting to the BPM domain. The results contribute to more transparency in business process models regarding knowledge changes. They provide better means to understand the dynamics and barriers of change processes.},
  language  = {en}
}