Author:
van der Aalst Wil M. P.,Jarke Matthias,Koren István,Quix Christoph
Abstract
AbstractDigitization in the field of production is fragmented in very different domains, ranging from materials to production technology to process and business models. Each domain comes with specialized knowledge, often incorporated into mathematical models. This heterogeneity makes it hard to naively exploit advances in data-driven machine learning that could facilitate situation adaptation and experience transfer. Innovative combinations of model-driven and data-driven solutions must be invented but also made comparable and interoperable to avoid ending up in information silos. In future World Wide Labs (WWLs), experiences can be shared, aggregated, and used for innovation. WWLs will be complex, evolving socio-technical networks of interconnected devices, software, data stores, and humans as users and contributors of expert knowledge and feedback. Integrating a large number of research labs, engineering, and production sites requires a capable cross-domain Internet of Production (IoP) infrastructure. The IoP project claims Digital Shadows (DSs) to offer a shared conceptual foundation for infrastructuring the IoP. In engineering, DSs were introduced as the data provision link to Digital Twins, whereas in computer science, DSs generalize the well-established concept of database views. In this chapter, we elaborate on the roles of DSs in infrastructuring the IoP from three perspectives: analytic functionality, conceptual organization, and technical networking. As an example where an integrative DS-like approach is already highly successful, we showcase the approach and infrastructure of the process mining field.
Publisher
Springer International Publishing
Reference35 articles.
1. van der Aalst W (2016) Process mining: data science in action. Springer, Berlin/Heidelberg
2. Lecture Notes in Computer Science;W van der Aalst,2021
3. van der Aalst W (2021b) Federated process mining: exploiting event data across organizational boundaries. In: Atukorala N, Chang C, Damiani E, Fu M, Spanoudakis G, Srivatsa M, Wang Z, Zhang J (eds) IEEE International Conference on Smart Data Services (SMDS 2021). IEEE, pp 1–7
4. van der Aalst W, Berti A (2020) Discovering object-centric petri nets. Fund Inform 175(1-4):1–40
5. Lecture Notes in Business Information Processing,2022