Abstract
Computer-aided engineering (CAE) applications support the digital transformation of the manufacturing industry. They facilitate virtual product development and product testing via computer simulations. CAE applications generate vast quantities of heterogeneous data. Domain experts struggle to access and analyze them, because such engineering data are not sufficiently described with metadata. In this paper, we characterize the CAE domain and identify unsolved challenges for a tailored data and metadata management. For instance, work activities in product development projects and their relationships to data are not represented explicitly in current metadata models. We propose a metadata model that addresses all challenges and provides a connected view on all CAE data, metadata, and work activities of development projects. We validate the feasibility of our metadata model through a prototypical implementation and its application to a real-world use case. This verifies that our metadata model addresses the CAE-specific challenges and this way eases the task of domain experts to exploit relevant data.
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Notes
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Medina/SDM: https://plm.t-systems-service.com/en/medina-sdm.
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OpenFOAM: https://www.openfoam.com/.
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Apache Hadoop: https://hadoop.apache.org.
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Azure Cosmos DB: https://azure.microsoft.com/services/cosmos-db/.
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Amazon Neptune https://aws.amazon.com/neptune/.
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Apache Tomcat: https://tomcat.apache.org.
References
Angles, R., Gutierrez, C.: Survey of Graph Database Models. ACM Comput. Surv. 40(1), 1–39 (2008)
Bose, R.: A conceptual framework for composing and managing scientific data lineage. In: Proceedings of the 14th International Conference on Scientific and Statistical Database Management, pp. 15–19. Edinburgh, Scotland, UK (2002)
Catalano, C.E., et al.: A product design ontology for enhancing shape processing in design workflows. J. Intell. Manuf. 20(5), 553–567 (2009)
Che, D., Safran, M., Peng, Z.: From big data to big data mining: challenges, issues, and opportunities. In: DASFAA 2013, Wuhan, China (2013)
Dankwort, C.W., et al.: Engineers’ CAx education-it’s not only CAD. Comput. Aided Des. 36(14), 1439–1450 (2004)
Davidson, S.B., Freire, J.: Provenance and scientific workflows: challenges and opportunities. In: Proceedings of SIGMOD, pp. 1345–1350. Vancouver, Canada (2008)
Fang, H.: Managing data lakes in big data era: what’s a data lake and why has it became popular in data management ecosystem. In: IEEE-CYBER 2015 (2015)
Gray, J., et al.: Scientific data management in the coming decade. SIGMOD Rec. 34(4), 34–41 (2005)
Hellerstein, J.M., et al.: Ground: a data context service. In: Proceedings of the 8th Biennial Conference on Innovative Data Systems Research, Chaminade, CA, USA (2017)
Herschel, M., Diestelkämper, R., Ben Lahmar, H.: A survey on provenance: what for? what form? what from? VLDB J. 26, 881–906 (2017)
Hirz, M., et al.: Overview of virtual product development. In: Integrated Computer-Aided Design in Automotive Development, pp. 25–50. Springer, Berlin, Heidelberg (2013) https://doi.org/10.1007/978-3-642-11940-8_2
Matthews, B., et al.: Using a core scientific metadata model in large-scale facilities. Int. J. Digital Curation 5(1), 106–118 (2010)
Miller, J.J.: Graph database applications and concepts with Neo4j. In: Proceedings of the Southern Association for Information Systems Conference, Atlanta, GA, USA (2013)
Object Management Group: Business Process Model and Notation (2014). https://www.omg.org/spec/BPMN/
Quix, C., Hai, R., Vatov, I.: Metadata extraction and management in data lakes with GEMMS. Complex Syst. Inf. Model. 9, 67–83 (2016)
Reimann, P., et al.: SIMPL - a framework for accessing external data in simulation workflows. In: BTW 2011, Kaiserslautern, Germany (2011)
Rodriguez, M.A., Neubauer, P.: Constructions from Dots and Lines. Bull. Am. Soc. Inf. Sci. Technol. 36(6), 35–41 (2010)
Sawadogo, P., et al.: Metadata systems for data lakes: models and features. In: ADBIS 2019 (2019)
Valle, M., et al.: Scientific data management for visualization implementation experience. In: Simulation und Visualisierung 2005 (SimVis 2005), pp. 347–354. SCS Publishing House e.V., Magdeburg, Germany (2005)
Vasilakis, C., et al.: A data warehouse environment for storing and analyzing simulation output data. In: Proceedings of of the WSC, vol. 1 (2004)
Viaene, S.: Data scientists aren’t domain experts. IT Prof. 15(6), 12–17 (2013)
Vinodh, S., Kuttalingam, D.: Computer-aided design and engineering as enablers of agile manufacturing. J. Manuf. Tech. Manage. 22, (2011)
Willis, C., Greenberg, J., White, H.: Analysis and synthesis of metadata goals for scientific data. JASIST 63(8), 1505–1520 (2012)
Acknowledgements
The authors thank the German Research Foundation (DFG), the Ministry of Science, Research and Arts of the State of Baden-Wurttemberg, and the MANN+HUMMEL GmbH for financial support within the Graduate School of Excellence advanced Manaufacturing Engineering.
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Ziegler, J., Reimann, P., Keller, F., Mitschang, B. (2021). A Metadata Model to Connect Isolated Data Silos and Activities of the CAE Domain. In: La Rosa, M., Sadiq, S., Teniente, E. (eds) Advanced Information Systems Engineering. CAiSE 2021. Lecture Notes in Computer Science(), vol 12751. Springer, Cham. https://doi.org/10.1007/978-3-030-79382-1_13
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