@book{Grum2022, author = {Grum, Marcus}, title = {Construction of a concept of neuronal modeling}, publisher = {Springer Fachmedien Wiesbaden}, address = {Wiesbaden}, isbn = {978-3-658-35998-0}, doi = {10.1007/978-3-658-35999-7}, pages = {lv, 848}, year = {2022}, abstract = {The business problem of having inefficient processes, imprecise process analyses and simulations as well as non-transparent artificial neuronal network models can be overcome by an easy-to-use modeling concept. With the aim of developing a flexible and efficient approach to modeling, simulating and optimizing processes, this paper proposes a flexible Concept of Neuronal Modeling (CoNM). The modeling concept, which is described by the modeling language designed and its mathematical formulation and is connected to a technical substantiation, is based on a collection of novel sub-artifacts. As these have been implemented as a computational model, the set of CoNM tools carries out novel kinds of Neuronal Process Modeling (NPM), Neuronal Process Simulations (NPS) and Neuronal Process Optimizations (NPO). The efficacy of the designed artifacts was demonstrated rigorously by means of six experiments and a simulator of real industrial production processes.}, language = {en} } @article{GrumHiesslMareschetal.2021, author = {Grum, Marcus and Hiessl, Werner and Maresch, Karl and Gronau, Norbert}, title = {Design of a neuronal training modeling language}, series = {AIS-Transactions on enterprise systems}, volume = {5}, journal = {AIS-Transactions on enterprise systems}, number = {1}, publisher = {GITO-Publ., Verl. f{\"u}r Industrielle Informationstechnik und Organisation}, address = {Berlin}, issn = {1867-7134}, doi = {10.30844/aistes.v5i1.20}, pages = {16}, year = {2021}, abstract = {As the complexity of learning task requirements, computer infrastruc- tures and knowledge acquisition for artificial neuronal networks (ANN) is in- creasing, it is challenging to talk about ANN without creating misunderstandings. An efficient, transparent and failure-free design of learning tasks by models is not supported by any tool at all. For this purpose, particular the consideration of data, information and knowledge on the base of an integration with knowledge- intensive business process models and a process-oriented knowledge manage- ment are attractive. With the aim of making the design of learning tasks express- ible by models, this paper proposes a graphical modeling language called Neu- ronal Training Modeling Language (NTML), which allows the repetitive use of learning designs. An example ANN project of AI-based dynamic GUI adaptation exemplifies its use as a first demonstration.}, language = {en} } @article{KlippertStolpmannGrumetal.2023, author = {Klippert, Monika and Stolpmann, Robert and Grum, Marcus and Thim, Christof and Gronau, Norbert and Albers, Albert}, title = {Knowledge transfer quality improvement}, series = {Procedia CIRP}, volume = {119}, journal = {Procedia CIRP}, publisher = {Elsevier}, address = {Amsterdam}, issn = {2212-8271}, doi = {10.1016/j.procir.2023.02.171}, pages = {919 -- 925}, year = {2023}, abstract = {Developing a new product generation requires the transfer of knowledge among various knowledge carriers. Several factors influence knowledge transfer, e.g., the complexity of engineering tasks or the competence of employees, which can decrease the efficiency and effectiveness of knowledge transfers in product engineering. Hence, improving those knowledge transfers obtains great potential, especially against the backdrop of experienced employees leaving the company due to retirement, so far, research results show, that the knowledge transfer velocity can be raised by following the Knowledge Transfer Velocity Model and implementing so-called interventions in a product engineering context. In most cases, the implemented interventions have a positive effect on knowledge transfer speed improvement. In addition to that, initial theoretical findings describe factors influencing the quality of knowledge transfers and outline a setting to empirically investigate how the quality can be improved by introducing a general description of knowledge transfer reference situations and principles to measure the quality of knowledge artifacts. To assess the quality of knowledge transfers in a product engineering context, the Knowledge Transfer Quality Model (KTQM) is created, which serves as a basis to develop and implement quality-dependent interventions for different knowledge transfer situations. As a result, this paper introduces the specifications of eight situation-adequate interventions to improve the quality of knowledge transfers in product engineering following an intervention template. Those interventions are intended to be implemented in an industrial setting to measure the quality of knowledge transfers and validate their effect.}, language = {en} } @inproceedings{GrumBlunkRojahnetal.2020, author = {Grum, Marcus and Blunk, Oliver and Rojahn, Marcel and Fettke, Peter and Gronau, Norbert}, title = {Research challenges of knowledge modelling and the outline of a research agenda}, series = {Knowledge in digital age : IFKAD 2020}, booktitle = {Knowledge in digital age : IFKAD 2020}, publisher = {The Arts of Business Institute}, address = {Matera, Italy}, isbn = {978-88-96687-13-0}, issn = {2280-787X}, year = {2020}, language = {en} } @inproceedings{GrumKlippertAlbersetal.2021, author = {Grum, Marcus and Klippert, Monika and Albers, Albert and Gronau, Norbert and Thim, Christof}, title = {Examining the quality of knowledge transfers}, series = {Proceedings of the Design Society}, volume = {1}, booktitle = {Proceedings of the Design Society}, publisher = {Cambridge University Press}, address = {Cambridge}, issn = {2732-527X}, doi = {10.1017/pds.2021.404}, pages = {1431 -- 1440}, year = {2021}, abstract = {Already successfully used products or designs, past projects or our own experiences can be the basis for the development of new products. As reference products or existing knowledge, it is reused in the development process and across generations of products. Since further, products are developed in cooperation, the development of new product generations is characterized by knowledge-intensive processes in which information and knowledge are exchanged between different kinds of knowledge carriers. The particular knowledge transfer here describes the identification of knowledge, its transmission from the knowledge carrier to the knowledge receiver, and its application by the knowledge receiver, which includes embodied knowledge of physical products. Initial empirical findings of the quantitative effects regarding the speed of knowledge transfers already have been examined. However, the factors influencing the quality of knowledge transfer to increase the efficiency and effectiveness of knowledge transfer in product development have not yet been examined empirically. Therefore, this paper prepares an experimental setting for the empirical investigation of the quality of knowledge transfers.}, language = {en} } @inproceedings{Grum2023, author = {Grum, Marcus}, title = {Learning representations by crystallized back-propagating errors}, series = {Artificial intelligence and soft computing}, booktitle = {Artificial intelligence and soft computing}, editor = {Rutkowski, Leszek and Scherer, Rafał and Korytkowski, Marcin and Pedrycz, Witold and Tadeusiewicz, Ryszard and Zurada, Jacek M.}, publisher = {Springer}, address = {Cham}, isbn = {978-3-031-42504-2}, doi = {10.1007/978-3-031-42505-9_8}, pages = {78 -- 100}, year = {2023}, abstract = {With larger artificial neural networks (ANN) and deeper neural architectures, common methods for training ANN, such as backpropagation, are key to learning success. Their role becomes particularly important when interpreting and controlling structures that evolve through machine learning. This work aims to extend previous research on backpropagation-based methods by presenting a modified, full-gradient version of the backpropagation learning algorithm that preserves (or rather crystallizes) selected neural weights while leaving other weights adaptable (or rather fluid). In a design-science-oriented manner, a prototype of a feedforward ANN is demonstrated and refined using the new learning method. The results show that the so-called crystallizing backpropagation increases the control possibilities of neural structures and interpretation chances, while learning can be carried out as usual. Since neural hierarchies are established because of the algorithm, ANN compartments start to function in terms of cognitive levels. This study shows the importance of dealing with ANN in hierarchies through backpropagation and brings in learning methods as novel ways of interacting with ANN. Practitioners will benefit from this interactive process because they can restrict neural learning to specific architectural components of ANN and can focus further development on specific areas of higher cognitive levels without the risk of destroying valuable ANN structures.}, language = {en} } @inproceedings{GrumThimRolingetal.2023, author = {Grum, Marcus and Thim, Christof and Roling, Wiebke and Sch{\"u}ffler, Arnulf and Kluge, Annette and Gronau, Norbert}, title = {AI case-based reasoning for artificial neural networks}, series = {Artificial intelligence and industrial applications}, volume = {771}, booktitle = {Artificial intelligence and industrial applications}, editor = {Masrour, Tawfik and El Hassani, Ibtissam and Barka, Noureddine}, publisher = {Springer}, address = {Cham}, isbn = {978-3-031-43523-2}, doi = {10.1007/978-3-031-43524-9_2}, pages = {17 -- 35}, year = {2023}, abstract = {Faced with the triad of time-cost-quality, the realization of production tasks under economic conditions is not trivial. Since the number of Artificial-Intelligence-(AI)-based applications in business processes is increasing more and more nowadays, the efficient design of AI cases for production processes as well as their target-oriented improvement is essential, so that production outcomes satisfy high quality criteria and economic requirements. Both challenge production management and data scientists, aiming to assign ideal manifestations of artificial neural networks (ANNs) to a certain task. Faced with new attempts of ANN-based production process improvements [8], this paper continues research about the optimal creation, provision and utilization of ANNs. Moreover, it presents a mechanism for AI case-based reasoning for ANNs. Experiments clarify continuously improving ANN knowledge bases by this mechanism empirically. Its proof-of-concept is demonstrated by the example of four production simulation scenarios, which cover the most relevant use cases and will be the basis for examining AI cases on a quantitative level.}, language = {en} } @inproceedings{GrumRappGronauetal.2019, author = {Grum, Marcus and Rapp, Simon and Gronau, Norbert and Albers, Albert}, title = {Accelerating knowledge}, series = {Business modeling and software design}, volume = {356}, booktitle = {Business modeling and software design}, editor = {Shishkov, Boris}, publisher = {Springer}, address = {Cham}, isbn = {978-3-030-24853-6}, doi = {10.1007/978-3-030-24854-3_7}, pages = {95 -- 113}, year = {2019}, abstract = {As knowledge-intensive processes are often carried out in teams and demand for knowledge transfers among various knowledge carriers, any optimization in regard to the acceleration of knowledge transfers obtains a great economic potential. Exemplified with product development projects, knowledge transfers focus on knowledge acquired in former situations and product generations. An adjustment in the manifestation of knowledge transfers in its concrete situation, here called intervention, therefore can directly be connected to the adequate speed optimization of knowledge-intensive process steps. This contribution presents the specification of seven concrete interventions following an intervention template. Further, it describes the design and results of a workshop with experts as a descriptive study. The workshop was used to assess the practical relevance of interventions designed as well as the identification of practical success factors and barriers of their implementation.}, language = {en} } @inproceedings{Grum2022, author = {Grum, Marcus}, title = {Context-aware, intelligent musical instruments for improving knowledge-intensive business processes}, series = {Business modeling and software design}, volume = {453}, booktitle = {Business modeling and software design}, editor = {Shishkov, Boris}, publisher = {Springer}, address = {Cham}, isbn = {978-3-031-11509-7}, doi = {10.1007/978-3-031-11510-3_5}, pages = {69 -- 88}, year = {2022}, abstract = {With shorter song publication cycles in music industries and a reduced number of physical contact opportunities because of disruptions that may be an obstacle for musicians to cooperate, collaborative time consumption is a highly relevant target factor providing a chance for feedback in contemporary music production processes. This work aims to extend prior research on knowledge transfer velocity by augmenting traditional designs of musical instruments with (I) Digital Twins, (II) Internet of Things and (III) Cyber-Physical System capabilities and consider a new type of musical instrument as a tool to improve knowledge transfers at knowledge-intensive forms of business processes. In a design-science-oriented way, a prototype of a sensitive guitar is constructed as information and cyber-physical system. Findings show that this intelligent SensGuitar increases feedback opportunities. This study establishes the importance of conversion-specific music production processes and novel forms of interactions at guitar playing as drivers of high knowledge transfer velocities in teams and among individuals.}, language = {en} } @article{PlonerHessGrumetal.2020, author = {Ploner, Tina and Hess, Steffen and Grum, Marcus and Drewe-Boss, Philipp and Walker, Jochen}, title = {Using gradient boosting with stability selection on health insurance claims data to identify disease trajectories in chronic obstructive pulmonary disease}, series = {Statistical methods in medical research}, volume = {29}, journal = {Statistical methods in medical research}, number = {12}, publisher = {Sage Publ.}, address = {London [u.a.]}, issn = {0962-2802}, doi = {10.1177/0962280220938088}, pages = {3684 -- 3694}, year = {2020}, abstract = {Objective We propose a data-driven method to detect temporal patterns of disease progression in high-dimensional claims data based on gradient boosting with stability selection. Materials and methods We identified patients with chronic obstructive pulmonary disease in a German health insurance claims database with 6.5 million individuals and divided them into a group of patients with the highest disease severity and a group of control patients with lower severity. We then used gradient boosting with stability selection to determine variables correlating with a chronic obstructive pulmonary disease diagnosis of highest severity and subsequently model the temporal progression of the disease using the selected variables. Results We identified a network of 20 diagnoses (e.g. respiratory failure), medications (e.g. anticholinergic drugs) and procedures associated with a subsequent chronic obstructive pulmonary disease diagnosis of highest severity. Furthermore, the network successfully captured temporal patterns, such as disease progressions from lower to higher severity grades. Discussion The temporal trajectories identified by our data-driven approach are compatible with existing knowledge about chronic obstructive pulmonary disease showing that the method can reliably select relevant variables in a high-dimensional context. Conclusion We provide a generalizable approach for the automatic detection of disease trajectories in claims data. This could help to diagnose diseases early, identify unknown risk factors and optimize treatment plans.}, language = {en} } @incollection{Grum2020, author = {Grum, Marcus}, title = {Managing human and artificial knowledge bearers}, series = {Business modeling and software design : 10th International Symposium, BMSD 2020, Berlin, Germany, July 6-8, 2020, Proceedings}, booktitle = {Business modeling and software design : 10th International Symposium, BMSD 2020, Berlin, Germany, July 6-8, 2020, Proceedings}, editor = {Shishkov, Boris}, publisher = {Springer International Publishing AG}, address = {Cham}, isbn = {978-3-030-52305-3}, doi = {10.1007/978-3-030-52306-0_12}, pages = {182 -- 201}, year = {2020}, abstract = {As part of the digitization, the role of artificial systems as new actors in knowledge-intensive processes requires to recognize them as a new form of knowledge bearers side by side with traditional knowledge bearers, such as individuals, groups, organizations. By now, artificial intelligence (AI) methods were used in knowledge management (KM) for knowledge discovery, for the reinterpreting of information, and recent works focus on the studying of different AI technologies implementation for knowledge management, like big data, ontology-based methods and intelligent agents [1]. However, a lack of holistic management approach is present, that considers artificial systems as knowledge bearers. The paper therefore designs a new kind of KM approach, that integrates the technical level of knowledge and manifests as Neuronal KM (NKM). Superimposing traditional KM approaches with the NKM, the Symbiotic Knowledge Management (SKM) is conceptualized furthermore, so that human as well as artificial kinds of knowledge bearers can be managed as symbiosis. First use cases demonstrate the new KM, NKM and SKM approaches in a proof-of-concept and exemplify their differences.}, language = {en} } @incollection{RojahnAmbrosBiruetal.2023, author = {Rojahn, Marcel and Ambros, Maximilian and Biru, Tibebu and Krallmann, Hermann and Gronau, Norbert and Grum, Marcus}, title = {Adequate basis for the data-driven and machine-learning-based identification}, series = {Artificial intelligence and soft computing}, booktitle = {Artificial intelligence and soft computing}, editor = {Rutkowski, Leszek and Scherer, Rafał and Korytkowski, Marcin and Pedrycz, Witold and Tadeusiewicz, Ryszard and Zurada, Jacek M.}, publisher = {Springer}, address = {Cham}, isbn = {978-3-031-42504-2}, doi = {10.1007/978-3-031-42505-9_48}, pages = {570 -- 588}, year = {2023}, abstract = {Process mining (PM) has established itself in recent years as a main method for visualizing and analyzing processes. However, the identification of knowledge has not been addressed adequately because PM aims solely at data-driven discovering, monitoring, and improving real-world processes from event logs available in various information systems. The following paper, therefore, outlines a novel systematic analysis view on tools for data-driven and machine learning (ML)-based identification of knowledge-intensive target processes. To support the effectiveness of the identification process, the main contributions of this study are (1) to design a procedure for a systematic review and analysis for the selection of relevant dimensions, (2) to identify different categories of dimensions as evaluation metrics to select source systems, algorithms, and tools for PM and ML as well as include them in a multi-dimensional grid box model, (3) to select and assess the most relevant dimensions of the model, (4) to identify and assess source systems, algorithms, and tools in order to find evidence for the selected dimensions, and (5) to assess the relevance and applicability of the conceptualization and design procedure for tool selection in data-driven and ML-based process mining research.}, language = {en} } @incollection{GrumGronau2020, author = {Grum, Marcus and Gronau, Norbert}, title = {Adaptable knowledge-driven information systems improving knowledge transfers}, series = {Business modeling and software design : 10th International Symposium, BMSD 2020, Berlin, Germany, July 6-8, 2020, Proceedings}, volume = {391}, booktitle = {Business modeling and software design : 10th International Symposium, BMSD 2020, Berlin, Germany, July 6-8, 2020, Proceedings}, editor = {Shishkov, Boris}, publisher = {Springer International Publishing}, address = {Cham}, isbn = {978-3-030-52305-3}, doi = {10.1007/978-3-030-52306-0_13}, pages = {202 -- 220}, year = {2020}, abstract = {A growing number of business processes can be characterized as knowledge-intensive. The ability to speed up the transfer of knowledge between any kind of knowledge carriers in business processes with AR techniques can lead to a huge competitive advantage, for instance in manufacturing. This includes the transfer of person-bound knowledge as well as externalized knowledge of physical and virtual objects. The contribution builds on a time-dependent knowledge transfer model and conceptualizes an adaptable, AR-based application. Having the intention to accelerate the speed of knowledge transfers between a manufacturer and an information system, empirical results of an experimentation show the validity of this approach. For the first time, it will be possible to discover how to improve the transfer among knowledge carriers of an organization with knowledge-driven information systems (KDIS). Within an experiment setting, the paper shows how to improve the quantitative effects regarding the quality and amount of time needed for an example manufacturing process realization by an adaptable KDIS.}, language = {en} } @incollection{GrumKotarskiAmbrosetal.2021, author = {Grum, Marcus and Kotarski, David and Ambros, Maximilian and Biru, Tibebu and Krallmann, Hermann and Gronau, Norbert}, title = {Managing knowledge of intelligent systems}, series = {Business modeling and software design : 11th International Symposium, BMSD 2021, Sofia, Bulgaria, July 5-7, 2021, Proceedings}, volume = {422}, booktitle = {Business modeling and software design : 11th International Symposium, BMSD 2021, Sofia, Bulgaria, July 5-7, 2021, Proceedings}, editor = {Shishkov, Boris}, publisher = {Springer International Publishing}, address = {Cham}, isbn = {978-3-030-79975-5}, doi = {10.1007/978-3-030-79976-2_5}, pages = {78 -- 96}, year = {2021}, abstract = {Since more and more business tasks are enabled by Artificial Intelligence (AI)-based techniques, the number of knowledge-intensive tasks increase as trivial tasks can be automated and non-trivial tasks demand human-machine interactions. With this, challenges regarding the management of knowledge workers and machines rise [9]. Furthermore, knowledge workers experience time pressure, which can lead to a decrease in output quality. Artificial Intelligence-based systems (AIS) have the potential to assist human workers in knowledge-intensive work. By providing a domain-specific language, contextual and situational awareness as well as their process embedding can be specified, which enables the management of human and AIS to ease knowledge transfer in a way that process time, cost and quality are improved significantly. This contribution outlines a framework to designing these systems and accounts for their implementation.}, language = {en} } @incollection{GrumGronau2021, author = {Grum, Marcus and Gronau, Norbert}, title = {Quantification of knowledge transfers}, series = {Business modeling and software design : 11th International Symposium, BMSD 2021, Sofia, Bulgaria, July 5-7, 2021, Proceedings}, volume = {422}, booktitle = {Business modeling and software design : 11th International Symposium, BMSD 2021, Sofia, Bulgaria, July 5-7, 2021, Proceedings}, editor = {Shishkov, Boris}, publisher = {Springer International Publishing}, address = {Cham}, isbn = {978-3-030-79975-5}, doi = {10.1007/978-3-030-79976-2_13}, pages = {224 -- 242}, year = {2021}, abstract = {Faced with the triad of time-cost-quality, the realization of knowledge-intensive tasks at economic conditions is not trivial. Since the number of knowledge-intensive processes is increasing more and more nowadays, the efficient design of knowledge transfers at business processes as well as the target-oriented improvement of them is essential, so that process outcomes satisfy high quality criteria and economic requirements. This particularly challenges knowledge management, aiming for the assignment of ideal manifestations of influence factors on knowledge transfers to a certain task. Faced with first attempts of knowledge transfer-based process improvements [1], this paper continues research about the quantitative examination of knowledge transfers and presents a ready-to-go experiment design that is able to examine quality of knowledge transfers empirically and is suitable to examine knowledge transfers on a quantitative level. Its use is proven by the example of four influence factors, which namely are stickiness, complexity, competence and time pressure.}, language = {en} } @incollection{GrumThimGronau2021, author = {Grum, Marcus and Thim, Christof and Gronau, Norbert}, title = {Aiming for knowledge-transfer-optimizing intelligent cyber-physical systems}, 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_16}, pages = {149 -- 157}, year = {2021}, abstract = {Since more and more production tasks are enabled by Industry 4.0 techniques, the number of knowledge-intensive production tasks increases as trivial tasks can be automated and only non-trivial tasks demand human-machine interactions. With this, challenges regarding the competence of production workers, the complexity of tasks and stickiness of required knowledge occur [1]. Furthermore, workers experience time pressure which can lead to a decrease in output quality. Cyber-Physical Systems (CPS) have the potential to assist workers in knowledge-intensive work grounded on quantitative insights about knowledge transfer activities [2]. By providing contextual and situational awareness as well as complex classification and selection algorithms, CPS are able to ease knowledge transfer in a way that production time and quality is improved significantly. CPS have only been used for direct production and process optimization, knowledge transfers have only been regarded in assistance systems with little contextual awareness. Embedding production and knowledge transfer optimization thus show potential for further improvements. This contribution outlines the requirements and a framework to design these systems. It accounts for the relevant factors.}, language = {en} } @misc{GrumKoerppenKorjahnetal.2022, author = {Grum, Marcus and K{\"o}rppen, Tim and Korjahn, Nicolas and Gronau, Norbert}, title = {Entwicklung eines KI-ERP-Indikators}, publisher = {Center for Enterprise Research, Universit{\"a}t Potsdam}, address = {Potsdam}, pages = {27}, year = {2022}, abstract = {K{\"u}nstliche Intelligenz (KI) gewinnt in zahlreichen Branchen rasant an Bedeutung und wird zunehmend auch in Enterprise Resource Planning (ERP)-Systemen als Anwendungsbereich erschlossen. Die Idee, dass Maschinen die kognitiven F{\"a}higkeiten des Menschen imitieren k{\"o}nnen, indem Wissen durch Lernen auf Basis von Beispielen in Daten, Informationen und Erfahrungen generiert wird, ist heute ein Schl{\"u}sselelement der digitalen Transformation. Jedoch charakterisiert der Einsatz von KI in ERP-System einen hohen Komplexit{\"a}tsgrad, da die KI als Querschnittstechnologie zu verstehen ist, welche in unterschiedlichen Unternehmensbereichen zum Einsatz kommen kann. Auch die Anwendungsgrade k{\"o}nnen sich dabei erheblich voneinander unterscheiden. Um trotz dieser Komplexit{\"a}t den Einsatz der KI in ERP-Systemen erfassen und systembezogen vergleichen zu k{\"o}nnen, wurde im Rahmen dieser Studie ein Reifegradmodell entwickelt. Dieses bildet die Ausgangsbasis zur Ermittlung der KI-Reife in ERP-Systemen und grenzt dabei die folgenden vier KI- bzw. systembezogenen Ebenen voneinander ab: 1) Technische M{\"o}glichkeiten, 2) Datenreife, 3) Funktionsreife und 4) Erkl{\"a}rf{\"a}higkeit des Systems.}, language = {de} } @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} } @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} } @article{GrumSultanowFriedmannetal.2021, author = {Grum, Marcus and Sultanow, Eldar and Friedmann, Daniel and Ulrich, Andre and Gronau, Norbert}, title = {Tools des Maschinellen Lernens}, publisher = {Gito}, address = {Berlin}, isbn = {978-3-95545-380-0}, doi = {10.30844/grum_2020}, pages = {143}, year = {2021}, abstract = {K{\"u}nstliche Intelligenz ist in aller Munde. Immer mehr Anwendungsbereiche werden durch die Auswertung von vorliegenden Daten mit Algorithmen und Frameworks z.B. des Maschinellen Lernens erschlossen. Dieses Buch hat das Ziel, einen {\"U}berblick {\"u}ber gegenw{\"a}rtig vorhandene L{\"o}sungen zu geben und dar{\"u}ber hinaus konkrete Hilfestellung bei der Auswahl von Algorithmen oder Tools bei spezifischen Problemstellungen zu bieten. Um diesem Anspruch gerecht zu werden, wurden 90 L{\"o}sungen mittels einer systematischen Literaturrecherche und Praxissuche identifiziert sowie anschließend klassifiziert. Mit Hilfe dieses Buches gelingt es, schnell die notwendigen Grundlagen zu verstehen, g{\"a}ngige Anwendungsgebiete zu identifizieren und den Prozess zur Auswahl eines passenden ML-Tools f{\"u}r das eigene Projekt systematisch zu meistern.}, language = {de} }