Abstract
Decision makers use models to understand and analyze a situation, to compare alternatives and to find solutions. Additionally, there are systems that support decision makers through data analysis, calculation or simulation. Typically, modeling languages for humans and machine are different from each other. While humans prefer graphical or textual models, machine-interpretable models have to be represented in a formal language. This chapter describes an approach to modeling that is both cognitively adequate for humans and processable by machines. In addition, the approach supports the creation and adaptation of domain-specific modeling languages. A metamodel which is represented as a formal ontology determines the semantics of the modeling language. To create a graphical modeling language, a graphical notation can be added for each class of the ontology. Every time a new modeling element is created during modeling, an instance for the corresponding class is created in the ontology. Thus, models for humans and machines are based on the same internal representation.
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References
APQC (2014) Process classification framework version 6.1.1
Azzini A, Braghin C, Damiani E, Zavatarelli, F (2013) Using semantic lifting for improving process mining: a data loss prevention system case study. SIMPDA, pp 62–73
C-SIG (2014) Cloud service level agreement standardization guidelines. EC Cloud Select Industry Group
De Angelis G, Pierantonio A, Polini A, Re B, Thönssen B, Woitsch R (2016) Modeling for learning in public administrations—the learn PAd approach. In: domain-specific conceptual modeling, Cham: Springer International Publishing, pp 575–594. https://doi.org/10.1007/978-3-319-39417-6_26
Dietz JLG (2006) Enterprise ontology. theory and methodology. Springer, Berlin Heidelberg
Emmenegger S, Hinkelmann K, Laurenzi E, Thönssen B, Witschel HF, Zhang C (2016). Workplace learning—providing recommendations of experts and learning resources in a context-sensitive and personalized manner. In: MODELSWARD 2016, Special session on learning modeling in complex organizations. Rome
Fill H-G, Karagiannis D (2016) On the conceptualisation of modelling methods using the ADOxx meta modelling platform. Enterp Model Inf Syst Architect - Int J Conceptual Mode 8(1): 4–25
Fill H-G, Schremser D, Karagiannis D (2013) A generic approach for the semantic annotation of conceptual models using a service-oriented architecture. Int J Knowled Manag 9(1):76–88. https://doi.org/10.4018/jkm.2013010105
Fowler M (2011) Domain-specific languages. Addison-Wesley, Upper Saddle River
Frank U (2010) Outline of a method for designing domain-specific modelling languages. University of Duisburg Essen, ICB
Gray J, Fisher K, Consel C, Karsai G, Mernik M, Tolvanen JP (2008) DSLs: the good, the bad, and the ugly. In: Conference on object oriented programming systems languages and applications archive. Nashville and {É}tats-Unis: ACM
Hinkelmann K, Gerber A, Karagiannis D, Thoenssen B, van der Merwe A, Woitsch R (2016a) A new paradigm for the continuous alignment of business and IT: Combining enterprise architecture modelling and enterprise ontology. Comput Ind 79: 77–86. https://doi.org/10.1016/j.compind.2015.07.009
Hinkelmann K, Kritikos K, Kurjakovic S, Lammel B, Woitsch R (2016b) A modelling environment for business process as a service. CAiSE 2016: Advanced Information Systems Engineering Workshops, Ljubljana, Slovenia, pp 181–192
Hinkelmann K, Kurjakovic S, Lammel B, Laurenzi E, Woitsch R (2016c) A semantically-enhanced modelling environment for business process as a service. In: Fourth international conference on enterprise systems ES2016, Melbourne, Australia, 2–3 November 2016
Höfferer P (2007) Achieving business process model interoperability using metamodels and ontologies. In: European conference on information systems. university of St. Gallen. (pp 1620–1631). http://www.dke.at/fileadmin/DKEHP/publikationen/metamodell/Hoefferer_BP_interoperability_ontologies.pdf
Hrgovcic V, Karagiannis D, Woitsch R (2013). Conceptual modeling of the organisational aspects for distributed applications: the semantic lifting approach. In: COMPSACW 2013, 2013 IEEE 37th annual computer software and applications conference workshops, pp 145–150. IEEE. https://doi.org/10.1109/compsacw.2013.17
Hudak P, Paul (1996) Building domain-specific embedded languages. ACM Comput Surveys, 28(4es), 196–es. https://doi.org/10.1145/242224.242477
Kappel G, Kapsammer E, Kargl H, Kramler G, Reiter T, Retschitzegger W et al (2006) Lifting metamodels to ontologies: a step to the semantic integration of modeling languages. In: Nierstrasz O, Whittle J, Harel D, Reggio G (Eds.), Model driven engineering languages and systems, Proceedings of the 9th international conference, MoDELS 2006 (LNCS 4199, pp 528–542). Genova, Italy: Springer
Karagiannis D, Kühn H (2002) Metamodelling platforms. In: Bauknecht K, Min Tjoa A, Quirchmayer G (Eds.), Proceedings of the third international conference EC-Web at DEXA 2002. Berlin: Springer
Karagiannis D, Woitsch R (2010) Knowledge Engineering in Business Process Management. Handbook on business process management 2. Springer, Berlin Heidelberg, pp 463–485
Kelly S, Tolvanen J-P (2008) Domain-specific modeling: Enabling full code generation. Wiley, Hoboken
Kramler G, Kappel G, Reiter T, Kapsammer E, Retschitzegger W, Schwinger W (2006) Towards a semantic infrastructure supporting model-based tool integration. In GaMMa’06: Proceedings of the 2006 international workshop on global integrated model management (pp 43–46). New York, NY, USA: ACM Press
Jonkers H, Van Buuren R, Arbab F, De Boer F, Bonsangue M, Iacob M, Enschede AN (2003). Towards a language for coherent enterprise architecture descriptions. https://pdfs.semanticscholar.org/546f/0891738f53a6639e863454d915a71094d9ce.pdf
Laurenzi E, Hinkelmann K, Reimer U, Van Der Merwe A, Sibold P, Endl R (2017). DSML4PTM: a domain-specific modelling language for patient transferal management. In ICEIS 2017—Proceedings of the 19th international conference on enterprise information systems vol. 3
Liao Y, Lezoche M, Panetto H, Boudjlida N, Loures ER (2015) Semantic annotation for knowledge explicitation in a product lifecycle management context: a survey. Comput Ind 71:24–34
Mernik M, Heering J, Sloane AM (2005) When and how to develop domain-specific languages. ACM Comput Surv 37(4):316–344. https://doi.org/10.1145/1118890.1118892
Nikles S, Brander S (2009) Separating conceptual and visual aspects in meta-modeling. In: Gerber A, Hinkelmann K, Kotze P, Reimer U, van der Merwe A (Eds.), Workshop on advanced enterprise architecture and repositories, Milano
OMG (2011) Business Process Model and Notation (BPMN) version 2.0. Needham, MA: object management group OMG. http://www.omg.org/spec/BPMN/2.0/PDF/
OMG (2014). OMG Meta Object Facility (MOF) Core Specification Version 2.4.2 (Vol. 2)
OMG (2016) Decision Model and Notation (DMN) V1.1. Object management group OMG. http://www.omg.org/spec/DMN/1.1
van Deursen A, Klint P, Visser J (2000) Domain-specific languages: an annotated bibliography. SIGPLAN Not 35(6):26–36. https://doi.org/10.1145/352029.352035
Woitsch R, Hinkelmann K, Juan Ferrer AM, Yuste JI (2016) Business Process as a Service (BPaaS): the smart BPaaS design environment. CAiSE 2016 industry track CEUR workshop proceedings, vol 1600. http://ceur-ws.org/Vol-1600, Ljubljana, Slovenia
W3C (2014). RDF Schema 1.1
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This research has received funding from the European Community’s Framework Programme for Research and Innovation HORIZON 2020 (ICT-07-2014) under grant agreement number 644690 (CloudSocket).
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Hinkelmann, K., Laurenzi, E., Martin, A., Thönssen, B. (2018). Ontology-Based Metamodeling. In: Dornberger, R. (eds) Business Information Systems and Technology 4.0. Studies in Systems, Decision and Control, vol 141. Springer, Cham. https://doi.org/10.1007/978-3-319-74322-6_12
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