Abstract
The conceptual modeling community and its subdivisions of enterprise modeling are increasingly investigating the potentials of applying artificial intelligence, in particular machine learning (ML), to tasks like model creation, model analysis, and model processing. A prerequisite—and currently a limiting factor for the community—to conduct research involving ML is the scarcity of openly available models of adequate quality and quantity. With the paper at hand, we aim to tackle this limitation by introducing an EA ModelSet, i.e., a curated and FAIR repository of enterprise architecture models that can be used by the community. We report on our efforts in building this data set and elaborate on the possibilities of conducting ML-based modeling research with it. We hope this paper sparks a community effort toward the development of a FAIR, large model set that enables ML research with conceptual models.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
- 2.
- 3.
- 4.
- 5.
- 6.
- 7.
- 8.
- 9.
- 10.
- 11.
- 12.
- 13.
- 14.
References
Ali, S.J., Guizzardi, G., Bork, D.: Enabling representation learning in ontology-driven conceptual modeling using graph neural networks. In: Indulska, M., Reinhartz-Berger, I., Cetina, C., Pastor, O. (eds.) Advanced Information Systems Engineering - 35th International Conference, CAiSE 2023, Zaragoza, Spain, 12–16 June 2023, Proceedings. LNCS, vol. 13901, pp. 278–294. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-34560-9_17
Barbosa, A.O., Santana, A., Hacks, S., von Stein, N.: A taxonomy for enterprise architecture analysis research. In: 21st International Conference on Enterprise Information Systems, ICEIS 2019, pp. 493–504. SciTePress (2019). https://doi.org/10.5220/0007692304930504
Barcelos, P.P.F., Sales, T.P., Fumagalli, M., et al.: A FAIR model catalog for ontology-driven conceptual modeling research. In: Ralyté, J., Chakravarthy, S., Mohania, M., Jeusfeld, M.A., Karlapalem, K. (eds.) 41st International Conference on Conceptual Modeling, ER 2022. LNCS, vol. 13607, pp. 3–17. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-17995-2_1
Bernabé, C., Sales, T.P., Schultes, E., et al.: A goal-oriented method for FAIRification planning (2023). https://doi.org/10.21203/rs.3.rs-3092538/v1
Bork, D., Ali, S.J., Dinev, G.M.: AI-enhanced hybrid decision management. Bus. Inf. Syst. Eng. 65(2), 179–199 (2023). https://doi.org/10.1007/s12599-023-00790-2
Bork, D., Ali, S.J., Roelens, B.: Conceptual modeling and artificial intelligence: a systematic mapping study. CoRR abs/2303.06758 (2023). https://doi.org/10.48550/arXiv.2303.06758
Borozanov, V., Hacks, S., Silva, N.: Using machine learning techniques for evaluating the similarity of enterprise architecture models - technical paper. In: Advanced Information Systems Engineering - 31st International Conference, pp. 563–578 (2019)
Corradini, F., Fornari, F., Polini, A., et al.: RePROSitory: a repository platform for sharing business process models and logs. In: Proceedings of the 1st Italian Forum on Business Process Management, pp. 13–18. CEUR-WS.org (2021)
Dumas, M., Rosa, M.L., Mendling, J., Reijers, H.A.: Fundamentals of BPM: model collections. http://fundamentals-of-bpm.org/process-model-collections/. Accessed 24 July 2023
Glaser, P.L., Sallinger, E., Bork, D.: EA ModelSet, July 2023. https://doi.org/10.5281/zenodo.8192011
Hinkelmann, K., Laurenzi, E., Martin, A., et al.: ArchiMEO: a standardized enterprise ontology based on the ArchiMate conceptual model. In: Proceedings of the 8th International Conference on Model-Driven Engineering and Software Development, MODELSWARD 2020, pp. 417–424. SCITEPRESS (2020). https://doi.org/10.5220/0009000204170424
López, J.A.H., Cuadrado, J.S.: An efficient and scalable search engine for models. Softw. Syst. Model. 21(5), 1715–1737 (2022). https://doi.org/10.1007/s10270-021-00960-4
López, J.A.H., Izquierdo, J.L.C., Cuadrado, J.S.: ModelSet: a dataset for machine learning in model-driven engineering. Softw. Syst. Model. 21(3), 967–986 (2022). https://doi.org/10.1007/s10270-021-00929-3
López, J.A.H., Izquierdo, J.L.C., Cuadrado, J.S.: Using the ModelSet dataset to support machine learning in model-driven engineering. In: Kühn, T., Sousa, V. (eds.) 25th International Conference on Model Driven Engineering Languages and Systems: Companion Proceedings, MODELS 2022, pp. 66–70. ACM (2022). https://doi.org/10.1145/3550356.3559096
Pezoa, F., Reutter, J.L., Suárez, F., et al.: Foundations of JSON schema. In: 25th International Conference on World Wide Web, WWW 2016, pp. 263–273. ACM (2016)
Raavikanti, S., Hacks, S., Katsikeas, S.: A recommender plug-in for enterprise architecture models. In: 25th International Conference on Enterprise Information Systems, ICEIS 2023, pp. 474–480. SCITEPRESS (2023). https://doi.org/10.5220/0011709000003467
Rahman, M.I., Panichella, S., Taibi, D.: A curated dataset of microservices-based systems. CoRR abs/1909.03249 (2019). http://arxiv.org/abs/1909.03249
Robles, G., Ho-Quang, T., Hebig, R., et al.: An extensive dataset of UML models in GitHub. In: 14th International Conference on Mining Software Repositories, MSR 2017, pp. 519–522. IEEE Computer Society (2017). https://doi.org/10.1109/MSR.2017.48
Schäfer, B., van der Aa, H., Leopold, H., Stuckenschmidt, H.: Sketch2BPMN: automatic recognition of hand-drawn BPMN models. In: La Rosa, M., Sadiq, S., Teniente, E. (eds.) CAiSE 2021. LNCS, vol. 12751, pp. 344–360. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-79382-1_21
Shilov, N., Othman, W., Fellmann, M., Sandkuhl, K.: Machine learning for enterprise modeling assistance: an investigation of the potential and proof of concept. Softw. Syst. Model. 22(2), 619–646 (2023). https://doi.org/10.1007/s10270-022-01077-y
da Silva Santos, L.O.B., Sales, T.P., Fonseca, C.M., Guizzardi, G.: Towards a conceptual model for the FAIR digital object framework. CoRR abs/2302.11894 (2023). https://doi.org/10.48550/arXiv.2302.11894
Sola, D., Warmuth, C., Schäfer, B., et al.: SAP Signavio Academic Models: a large process model dataset. In: Montali, M., Senderovich, A., Weidlich, M. (eds.) Process Mining Workshops - ICPM 2022 International Workshops. LNBIP, vol. 468, pp. 453–465. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-27815-0_33
Wilkinson, M.D., Dumontier, M., Aalbersberg, I.J., et al.: The FAIR guiding principles for scientific data management and stewardship. Sci. Data 3(1), 160018 (2016). https://doi.org/10.1038/sdata.2016.18
Zhi, Q., Zhou, Z.: Empirically modeling enterprise architecture using ArchiMate. Comput. Syst. Sci. Eng. 40(1), 357–374 (2022). https://doi.org/10.32604/csse.2022.018759
Acknowledgements
This work has been partially funded through the Erasmus+ KA220-HED project “Digital Platform Enterprise” (project no. 2021-1-RO01-KA220-HED-000027576) and the Vienna Science and Technology Fund (WWTF) (10.47379/VRG18013).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 IFIP International Federation for Information Processing
About this paper
Cite this paper
Glaser, PL., Sallinger, E., Bork, D. (2024). EA ModelSet – A FAIR Dataset for Machine Learning in Enterprise Modeling. In: Almeida, J.P.A., Kaczmarek-Heß, M., Koschmider, A., Proper, H.A. (eds) The Practice of Enterprise Modeling. PoEM 2023. Lecture Notes in Business Information Processing, vol 497. Springer, Cham. https://doi.org/10.1007/978-3-031-48583-1_2
Download citation
DOI: https://doi.org/10.1007/978-3-031-48583-1_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-48582-4
Online ISBN: 978-3-031-48583-1
eBook Packages: Computer ScienceComputer Science (R0)
