International Journal of Computer Science & Network Security

International Journal of Computer Science & Network Security (국제컴퓨터통신보호논문지학회)
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Conceptual Data Modeling: Entity-Relationship Models as Thinging Machines

  • Received : 2021.09.05
  • Published : 2021.09.30
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Abstract

Data modeling is a process of developing a model to design and develop a data system that supports an organization's various business processes. A conceptual data model represents a technology-independent specification of structure of data to be stored within a database. The model aims to provide richer expressiveness and incorporate a set of semantics to (a) support the design, control, and integrity parts of the data stored in data management structures and (b) coordinate the viewing of connections and ideas on a database. The described structure of the data is often represented in an entity–relationship (ER) model, which was one of the first data-modeling techniques and is likely to continue to be a popular way of characterizing entity classes, attributes, and relationships. This paper attempts to examine the basic ER modeling notions in order to analyze the concepts to which they refer as well as ways to represent them. In such a mission, we apply a new modeling methodology (thinging machine; TM) to ER in terms of its fundamental building constructs, representation entities, relationships, and attributes. The goal of this venture is to further the understanding of data models and enrich their semantics. Three specific contributions to modeling in this context are incorporated: (a) using the TM model's five generic actions to inject processing in the ER structure; (b) relating the single ontological element of TM modeling (i.e., a thing/machine or thimac) to ER entities and relationships; and (c) proposing a high-level integrated, extended ER model that includes structural and time-oriented notions (e.g., events or behavior).

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References

  1. Silvert, W.: Modelling As a Discipline. International Journal of General Systems 30(3), 261-282 (2000). DOI: 10.1080/03081070108960709
  2. Beal, J.: From the Mechanistic Modeling of Signaling Pathways in Cancer to the Interpretation of Models and Their Contributions: Clinical Applications and Statistical Evaluation. Ph.D. Thesis, University of Paris, September 2020.
  3. Frigg, R., Hartmann, S.: Models in Science. Zalta, E.N. (ed.) The Stanford Encyclopedia of Philosophy, Spring 2020 ed. Volume(issue), xx-yy (Year). https://plato.stanford.edu/archives/spr2020/entries/models-science/.
  4. Suppes, P.: Models of Data. Logic, Methodology and Philosophy of Science. In: Proc. of the 1960 International Conference, Nagel, E., Suppes, P., Tarski, A. (eds.) & 7 more. Stanford University Press (1962)
  5. Marciniak, J.J. (ed.): Encyclopedia of Software Engineering, 2nd ed.. John Wiley and Sons, Inc., New York (2001)
  6. Eckert, C.M., Stacey, M.K.: What Is a Process Model? Reflections on the Epistemology of Design Process Models. In: Eckert, C.M., Stacey, M.K. (eds.) Modelling and Management of Engineering Processes, pp. 3-14. Springer, London (2010). DOI:10.1007/978-1-84996-199-8
  7. Elmasri, R., Navathe, S.B.: Fundamentals of Database Systems, 7th ed. Pearson, Boston (2016)
  8. Peckham, J., Maryanski, F.: Semantic Data Models. ACM Computing Surveys 20(3), 153-189 (1988). DOI:10.1145/62061.62062
  9. Yarlagadda, R. T., Syed, H. H.: Data Models in Information Technology. International Journal of Innovations in Engineering Research and Technology 3(2), xx-yy (2016).
  10. West, M.: Some Types and Uses of Data Models. In: Developing High Quality Data Models, pp. 23-36. Elsevier Inc. (2011). DOI:10.1016/C2009-0-30508-5
  11. Kohen, H., Dori, D.: Improving Conceptual Modeling with Object-Process Methodology Stereotypes. Appl. Sci. 2021(11), 2301 (Year). DOI:10.3390/ app11052301
  12. Al-Fedaghi, S.: UML Modeling to TM Modeling and Back. International Journal of Computer Science and Network Security 21(1), 84-96 (2021). DOI:10.22937/IJCSNS.2021.21.1.13
  13. Tupper, C.D.: Model Constructs and Model Types. In: Data Architecture From Zen to Reality, pp. 207-221. Elsevier Inc. (2011). DOI:10.1016/B978-0-12-385126-0.00011-5
  14. Sparx Systems: Database Models. Enterprise Architect (2017). https://sparxsystems.com/resources/user-guides/model-domains/database-models.pdf
  15. Beconyte, G.B., Beconyte, G.: Entity-Relationship Modelling and Cartographic Transcription. Geodezija ir Kartografija 29(3), 78-83. DOI: 10.1080/13921541.2003.10552997
  16. Nyerges, T.: Conceptual Data Models. Wilson, J.P. (ed.), The Geographic Information Science and Technology Body of Knowledge volume(issue), xx-yy (2017). DOI:10.22224/gistbok/2017.1.3
  17. Spaccapietra, S., Parent, C., Vangenot, C., Cullot, N.: On Using Conceptual Modeling for Ontologies. In: Proc. Web Information Systems Workshops. LNCS 3307. Springer-Verslag (2004)
  18. Osborne, J.M.: Planning, Philosophy of Filemaker. Blog (2017). https://philosophyoffilemaker.com/zoom.php?recid=421
  19. Kashyap, M.M.: Likeness Between Ranganathan's Postulations- Based Approach to Knowledge Classification and Entity Relationship Data Modelling Approach. Knowledge Organization 30(1), 1-19 (2003.
  20. Thalheim, B.: Entity-Relationship Modeling: Foundations of Database Technology. Springer-Verlag, Berlin (2000)
  21. West, M.: Developing High Quality Data Models. EPISTLE (the European Process Industries STEP Technical Liaison Executive), ver. 2.0 2(1), (2003)
  22. Hitchman, S.: The Entity Relationship Model and Practical Data Modeling. Journal of Conceptual Modeling volume(31), xx-yy (2004)
  23. Chen, P.P.: Entity-Relationship Modeling: Historical Events, Future Trends, and Lessons Learned. In: Software Pioneers: Contributions to Software Engineering. Broy, M., Denert, E. (eds.), pp. 100-114. Springer-Verlag, Berlin (2002)
  24. Leonard, H. S., Goodman, N.: The Calculus of Individuals and Its Uses. The Journal of Symbolic Logic 5(2), 45-55 (1940) https://doi.org/10.2307/2266169
  25. Milton, S.K., Kazmierczak, E.: An Ontology of Data Modelling Languages: A Study Using a Common-Sense Realistic Ontology. Journal of Database Management 15(2), 19-38 (2004). DOI: 10.4018/jdm.2004040102
  26. Green, T.J.: Conceptual Modeling Using the Entity-Relationship Model. Lecture notes, Department of Computer Science, University of California, Davis (Year). https://web.cs.ucdavis.edu/~green/courses/ecs165a-w11/2-er.pdf, accessed Aug. 1, 2021.
  27. Karukonda, S.K.: Entity Relationship Approach to Knowledge-Based Systems. LSU Historical Dissertations and Theses, 4575 (1988). https://digitalcommons.lsu.edu/gradschool_disstheses/4575
  28. Song, I.Y., Chen, P.P.: Entity Relationship Model. Ling, L., Tamer Ozsu, M. (eds.) Encyclopedia of Database Systems, 2009 ed. volume(issue), 148 (Year). DOI:10.1007/978-0-387-39940-9
  29. van Fraassen, B.C.: An Introduction to the Philosophy of Time and Space. Nousoul Digital Publishers, Location (2015). https://www.princeton.edu/~fraassen/BvF%20-%20IPTS.pdf
  30. Mason, S.J.: Feedback Theory: Further Properties of Signal Flow Graphs. Proc. IRE 44(7), 920-926 (1956). https://doi.org/10.1109/JRPROC.1956.275147
  31. Gruber, T.R.: Toward Principles in the Design of Ontologies Used for Knowledge Sharing. Technical Report 93-04, Knowledge Systems Laboratory, Palo Alto, CA, Stanford University.
  32. Dhabe, D.P., Patwardhan, M.S., Pundlik, S.P., Dhore, M., Barbadekar, B.V., Abhyankar, H.K.: Articulated Entity Relationship (AER) Diagram for Complete Automation of Relational Database Normalization. International Journal of Database Management Systems 2(2), 84-100 (2010). DOI: 10.5121/ijdms.2010.2206
  33. Ruppel, S.R.: System Behavior Models: A Survey of Approaches. Thesis, Naval Postgraduate School, Monterey, CA, June 2016. https://apps.dtic.mil/sti/pdfs/AD1026811.pdf