Abstract
The paper proposes a new frontier for conceptual modeling – universal conceptual modeling (UCM) – defined as conceptual modeling that is general-purpose and accessible to anyone. For the purposes of the discussion, we envision a non-existent, hypothetical universal conceptual modeling language, which we call Datish (as in English or Spanish for data). We focus on the need for a universal conceptual data model to explain the expected benefits of UCM. Datish can facilitate the design of many different applications, including relational databases, NoSQL databases, data lakes, and artificial intelligence systems, and enable use by a broad range of users. To pave the way for rigorous development of such a language, we provide a theoretical basis for Datish in the form of a set of universal conceptual modeling principles: flexibility, accessibility, ubiquity, minimalism, primitivism, and modularity. We apply these principles to illustrate their usefulness and to identify future research opportunities.
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References
Atzeni, P., et al.: The relational model is dead, SQL is dead, and I don’t feel so good myself. ACM SIGMOD Rec. 42(1), 64–68 (2013)
Azevedo, C.L., et al.: Modeling resources and capabilities in enterprise architecture: a well-founded ontology-based proposal for ArchiMate. Inf. Syst. 54, 235–262 (2015)
Bjørner, D.: Domain Science and Engineering: A Foundation for Software Development. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-73484-8
Blaut, J.M., et al.: Mapping as a cultural and cognitive universal. Ann. Assoc. Am. Geogr. 93(1), 165–185 (2003)
Bork, D.: Conceptual modeling and artificial intelligence: challenges and opportunities for enterprise engineering. In: Aveiro, D., Proper, H.A., Guerreiro, S., de Vries, M. (eds.) Enterprise Engineering Working Conference, pp. 3–9. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-11520-2_1
Bugiotti, F., et al.: Database design for NoSQL systems. In: Yu, E., Dobbie, G., Jarke, M., Purao, S. (eds.) Conceptual Modeling, pp. 223–231. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-12206-9_18
Burton-Jones, A., Weber, R.: Building conceptual modeling on the foundation of ontology. In: Computing Handbook: Information Systems and Information Technology, Boca Raton, FL, United States, pp. 15.1–15.24 (2014)
Castellanos, A., et al.: Basic classes in conceptual modeling: theory and practical guidelines. J. Assoc. Inf. Syst. 21(4), 1001–1044 (2020)
Chatziantoniou, D., Kantere, V.: Data virtual machines: data-driven conceptual modeling of big data infrastructures. Presented at the EDBT/ICDT Workshops (2020)
Chatziantoniou, D., Kantere, V.: Data virtual machines: enabling data virtualization. In: Rezig, E.K., et al. (eds.) Heterogeneous Data Management, Polystores, and Analytics for Healthcare, pp. 3–13. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-93663-1_1
Chomsky, N.: Knowledge of Language: Its Nature, Origin, and Use. Greenwood Publishing Group, Westport (1986)
Chua, C.E.H., et al.: Data management. MIS Q. 1–10 (2022)
Codd, E.F.: A relational model of data for large shared data banks. Commun. ACM 13(6), 377–387 (1970)
Compagnucci, I., Corradini, F., Fornari, F., Re, B.: Trends on the usage of BPMN 2.0 from publicly available repositories. In: Buchmann, R.A., Polini, A., Johansson, B., Karagiannis, D. (eds.) BIR 2021. LNBIP, vol. 430, pp. 84–99. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-87205-2_6
De Carlo, G., et al.: Rethinking model representation-a taxonomy of advanced information visualization in conceptual modeling. In: Ralyté, J., Chakravarthy, S., Mohania, M., Jeusfeld, M.A., Karlapalem, K. (eds.) Conceptual Modeling, pp. 35–51. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-17995-2_3
Dehaene, S., et al.: Symbols and mental programs: a hypothesis about human singularity. Trends Cogn. Sci. (2022)
Dupré, J.: A process ontology for biology. Philos. Mag. 67, 81–88 (2014)
Elahi, H., et al.: Pleasure or pain? An evaluation of the costs and utilities of bloatware applications in android smartphones. J. Netw. Comput. Appl. 157, 102578 (2020)
Eriksson, O., et al.: The case for classes and instances-a response to representing instances: the case for reengineering conceptual modelling grammars. Eur. J. Inf. Syst. 28(6), 681–693 (2019)
Fettke, P., Reisig, W.: Systems mining with heraklit: the next step. In: Di Ciccio, C., Dijkman, R., del Río Ortega, A., Rinderle-Ma, S. (eds.) BPM 2022 Forum, pp. 89–104. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-16171-1_6
Germonprez, M., et al.: A theory of tailorable technology design. J. Assoc. Inf. Syst. 8(6), 351–367 (2007)
Giebler, C., Gröger, C., Hoos, E., Schwarz, H., Mitschang, B.: Modeling data lakes with data vault: practical experiences, assessment, and lessons learned. In: Laender, A.H.F., Pernici, B., Lim, E.-P., de Oliveira, J.P.M. (eds.) ER 2019. LNCS, vol. 11788, pp. 63–77. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-33223-5_7
Goddard, C.: Semantic theory and semantic universals. In: Semantic and Lexical Universals, pp. 7–29 (1994)
Gonzalez-Perez, C.: How ontologies can help in software engineering. In: Cunha, J., Fernandes, J.P., Lämmel, R., Saraiva, J., Zaytsev, V. (eds.) GTTSE 2015. LNCS, vol. 10223, pp. 26–44. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-60074-1_2
Gregor, S., et al.: The anatomy of a design principle. J. Assoc. Inf. Syst. 21(6), 1622–1652 (2020)
Guarino, N., Guizzardi, G.: In the defense of ontological foundations for conceptual modeling. Scand. J. Inf. Syst. 18(1), 115–126 (2006)
Guizzardi, G.: Ontological foundations for structural conceptual models. Telematics Instituut Fundamental Research Series, Enschede, The Netherlands (2005)
Guizzardi, G., et al.: Towards ontological foundations for conceptual modeling: the unified foundational ontology (UFO) story. Appl. Ontol. 10(3–4), 259–271 (2015)
Harman, G.: Object-Oriented Ontology: A New Theory of Everything. Penguin UK, London (2018)
Henderson-Sellers, B.: Why philosophize; why not just model? In: Johannesson, P., Lee, M.L., Liddle, S.W., Opdahl, A.L., López, Ó.P. (eds.) ER 2015. LNCS, vol. 9381, pp. 3–17. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-25264-3_1
Hvalshagen, M., et al.: Empowering users with narratives: examining the efficacy of narratives for understanding data-oriented conceptual models. Inf. Syst. Res. 1–38 (2023)
Iacub, P.: Software ERP: El nuevo Gran Hermano de las organizaciones. Autores de Argentina, Buenos Aires (2015). https://bit.ly/3phEmbX
Kastrup, B.: An ontological solution to the mind-body problem. Philosophies 2(2), 10 (2017)
Kaur, K., Rani, R.: Modeling and querying data in NoSQL databases. In: 2013 IEEE International Conference on Big Data, pp. 1–7 IEEE (2013)
Lima, L., et al.: An integrated semantics for reasoning about SysML design models using refinement. Softw. Syst. Model. 16(3), 875–902 (2015). https://doi.org/10.1007/s10270-015-0492-y
Lorenzatti, A., Abel, M., Fiorini, S.R., Bernardes, A.K., dos Santos Scherer, C.M.: Ontological primitives for visual knowledge. In: da Rocha Costa, A.C., Vicari, R.M., Tonidandel, F. (eds.) SBIA 2010. LNCS (LNAI), vol. 6404, pp. 1–10. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-16138-4_1
Lukyanenko, R., et al.: Artifact sampling: using multiple information technology artifacts to increase research rigor. In: Proceedings of the 51st Hawaii International Conference on System Sciences (HICSS 2018), Big Island, Hawaii, pp. 1–12 (2018)
Lukyanenko, R., Storey, V.C., Pastor, O.: Foundations of information technology based on Bunge’s systemist philosophy of reality. Softw. Syst. Model. 20(4), 921–938 (2021). https://doi.org/10.1007/s10270-021-00862-5
Lukyanenko, R., et al.: Representing instances: the case for reengineering conceptual modeling grammars. Eur. J. Inf. Syst. 28(1), 68–90 (2019)
Lukyanenko, R., et al.: System: a core conceptual modeling construct for capturing complexity. Data Knowl. Eng. 141, 1–29 (2022)
Lukyanenko, R., Parsons, J.: Beyond micro-tasks: research opportunities in observational crowdsourcing. J. Database Manag. (JDM) 29(1), 1–22 (2018)
Mayr, H.C., Thalheim, B.: The triptych of conceptual modeling. Softw. Syst. Model. 20(1), 7–24 (2020). https://doi.org/10.1007/s10270-020-00836-z
Miller, G.: The magical number seven, plus or minus two: some limits on our capacity for processing information. Psychol. Rev. 63, 81–97 (1956)
Moody, D.L.: The “physics” of notations: toward a scientific basis for constructing visual notations in software engineering. IEEE Trans. Softw. Eng. 35(6), 756–779 (2009)
Muehlen, M., Recker, J.: How much language is enough? Theoretical and practical use of the business process modeling notation. In: Bubenko, J., Krogstie, J., Pastor, O., Pernici, B., Rolland, C., Sølvberg, A. (eds.) Seminal Contributions to Information Systems Engineering, pp. 429–443. Springer, Cham (2013). https://doi.org/10.1007/978-3-642-36926-1_35
Mylopoulos, J.: Information modeling in the time of the revolution. Inf. Syst. 23(3–4), 127–155 (1998)
Norman, D.A.: The Design of Everyday Things. Bsic Books, New York, NY (2002)
Noth, W.: Handbook of Semiotics. Indiana University Press, Bloomington (1990)
Parsons, J., Wand, Y.: Emancipating instances from the tyranny of classes in information modeling. ACM Trans. Database Syst. 25(2), 228–268 (2000)
Partridge, C., et al.: Are conceptual models concept models? Presented at the International Conference on Conceptual Modeling (2013)
Recker, J.: BPMN research: what we know and what we don’t know. In: Mendling, J., Weidlich, M. (eds.) BPMN 2012. LNBIP, vol. 125, pp. 1–7. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-33155-8_1
Recker, J., et al.: From representation to mediation: a new agenda for conceptual modeling research in a digital world. MIS Q. 45(1), 269–300 (2021)
Roberts, S.: Is Geometry a Language That Only Humans Know? (2022). https://www.nytimes.com/2022/03/22/science/geometry-math-brain-primates.html
Rosch, E., et al.: Basic objects in natural categories. Cogn. Psychol. 8(3), 382–439 (1976)
Sablé-Meyer, M., et al.: A language of thought for the mental representation of geometric shapes. Cogn. Psychol. 139, 101527 (2022)
Sablé-Meyer, M., et al.: Sensitivity to geometric shape regularity in humans and baboons: a putative signature of human singularity. Proc. Natl. Acad. Sci. 118, 16, e2023123118 (2021)
Stea, D., et al.: Mapping as a cultural universal. In: Portugali, J. (ed.) The Construction of Cognitive Maps, pp. 345–360. Springer, Dordrecht (1996)
Storey, V.C., et al.: Conceptual modeling: topics, themes, and technology trends. ACM Comput. Surv. (2023)
Teigland, R., Power, D.: The immersive internet: reflections on the entangling of the virtual with society, politics and the economy. Palgrave Macmillan, New York (2013)
Wand, Y., Weber, R.: On the ontological expressiveness of information systems analysis and design grammars. Inf. Syst. J. 3(4), 217–237 (1993)
Wand, Y., Weber, R.: Research commentary: Information systems and conceptual modeling - a research agenda. Inf. Syst. Res. 13(4), 363–376 (2002)
Western, P.: Why the majority of data projects fail: the case for a Universal Data Language. https://snowplow.io/blog/project-failure-universal-data-language/. Accessed 09 Jan 2023
Wyssusek, B., Zaha, J.M.: Towards a pragmatic perspective on requirements for conceptual modeling methods. Presented at the EMMSAD (2007)
Zittrain, J.: The Future of the Internet–and How To Stop It. Yale University Press, New Haven (2008)
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Lukyanenko, R., Parsons, J., Storey, V.C., Samuel, B.M., Pastor, O. (2023). Principles of Universal Conceptual Modeling. In: van der Aa, H., Bork, D., Proper, H.A., Schmidt, R. (eds) Enterprise, Business-Process and Information Systems Modeling. BPMDS EMMSAD 2023 2023. Lecture Notes in Business Information Processing, vol 479. Springer, Cham. https://doi.org/10.1007/978-3-031-34241-7_12
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