Skip to main content

Methodologies for Semi-automated Conceptual Data Modeling from Requirements

  • Conference paper
  • First Online:
Book cover Conceptual Modeling (ER 2015)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9381))

Included in the following conference series:

Abstract

Conceptual modeling is the foundation of system analysis and design methodologies. It is challenging because it requires a clear understanding of an application domain and the ability to translate the requirement specification into a conceptual data model. Semi-automated conceptual data modeling is a process of using an intelligent tool to aid the modeler for the purpose of building a quality conceptual data model. In this paper, we first present six categories of methodologies that can be used for developing conceptual data models. We then describe the characteristics of each category, compare these characteristics, and present related work of each category. We finally suggest a framework for semi-automatically generating conceptual data models from requirements and suggest challenging research topics.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Aguilera, D., García-Ranea, R., Gómez, C., Olivé, A.: An eclipse plugin for validating names in UML conceptual schemas. In: De Troyer, O., Medeiros, C.B., Billen, R., Hallot, P., Simitsis, A., Van Mingroot, H. (eds.) ER 2011. LNCS, vol. 6999, pp. 323–327. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  2. Aguilera, D., Gómez, C., Olivé, A.: A method for the definition and treatment of conceptual schema quality issues. In: Atzeni, P., Cheung, D., Ram, S. (eds.) ER 2012. LNCS, vol. 7532, pp. 501–514. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  3. Ambriola, V., Gervasi, V.: On the systematic analysis of natural language requirements with circe. Autom. Softw. Eng. 13, 107–167 (2006)

    Article  Google Scholar 

  4. Alexander, C.: The Timeless Way of Building. Oxford University Press, New York (1979)

    Google Scholar 

  5. Anthony, S., Mellarkod, V.: Data modeling patterns: a method and evaluation. In: Proceedings of the Fifteenth Americas Conference on Information Systems, San Francisco, California (2009)

    Google Scholar 

  6. Atkinson, C., Kuhne, T.: Model-driven development: a metamodeling foundation. IEEE Softw. 20(5), 36–41 (2003)

    Article  Google Scholar 

  7. Batra, D.: Cognitive complexity in data modeling: causes and recommendations. Requirements Eng. 12(4), 231–244 (2007)

    Article  Google Scholar 

  8. Blaha, M.: Patterns of Data Modeling. CRC Press, Boca Raton (2010)

    Google Scholar 

  9. Buchholz, E., Cyriaks, H., Dsterhft, A., Mehlan, H., Thalheim, B.: Applying a natural language dialogue tool for designing databases. In: Proceedings of the first International Workshop on Applications of Natural Language to Databases (NLDB 1995) (1995)

    Google Scholar 

  10. Burton-Jones, A., Meso, P.: How good are these UML diagrams? An empirical test of the Wand and Weber good decomposition model. In: ICIS 2002 Proceedings, 10 (2002)

    Google Scholar 

  11. Chaiyasut, P., Shanks, G.: Conceptual data modeling process: a study of novice and expert data modelers. In: Proceedings of the 1st International Conference on Object-Role Modeling, Australia, University of Queensland (1994)

    Google Scholar 

  12. Chen, P.: English sentence structure and entity-relationship diagram. Inf. Sci. 1(1), 127–149 (1983)

    Article  Google Scholar 

  13. Chen, P.: The entity-relationship model: toward a unified view of data. ACM Trans. Database Syst. 1(1), 9–36 (1976)

    Article  Google Scholar 

  14. Choobineh, J., Lo, A.: CABSYDD: case-based system for database design. J. Manag. Inf. Syst. 21(3), 242–253 (2004)

    Google Scholar 

  15. Coad, P., North, D., Mayfield, M.: Object Models – Strategies, Pattern, and Applications. Yourdon Press, Englewood Cliffs (1995)

    Google Scholar 

  16. Conesa, J., Storey, V.C., Sugumaran, V.: Experiences using the ResearchCyc upper level ontology. In: Kedad, Z., Lammari, N., Métais, E., Meziane, F., Rezgui, Y. (eds.) NLDB 2007. LNCS, vol. 4592, pp. 143–155. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  17. Corcho, O., Fernandez-Lopez, M., Gomez-Perez, A.: Methodologies, tools and languages for building ontologies: where is their meeting point? Data Knowl. Eng. 46, 41–64 (2003)

    Article  Google Scholar 

  18. Deeptimahanti, D.K, Sanyal, R.: Semi-automatic generation of UML models from natural language requirements. In: Proceedings of the 4th India Software Engineering Conference (ISEC 2011), pp. 165–174 (2011)

    Google Scholar 

  19. Dehne, F., Steuten, A., van de Riet, R.P.: WordNet++: a lexicon for the color-X method. Data Knowl. Eng. 38(1), 3–29 (2001)

    Article  MATH  Google Scholar 

  20. El-Ghalayini, H., Odeh, M., McClatchey, R.: Engineering conceptual data models from domain ontologies: a critical evaluation. Int. J. Inf. Technol. Web Eng. 2(1), 57–70 (2006)

    Article  Google Scholar 

  21. Embley, D.: Toward semantic understanding: an approach based on information extraction ontologies. In: Proceedings of the 15th Australian Database Conference, Denedin, New Zealand, pp. 3–12 (2004)

    Google Scholar 

  22. Evermann, J., Wand, Y.: Towards ontologically-based semantics for UML constructs. In: Kunii, H.S., Jajodia, S., Sølvberg, A. (eds.) ER 2001. LNCS, vol. 2224, pp. 354–367. Springer, Heidelberg (2001)

    Chapter  Google Scholar 

  23. Fellbaum, C.: WordNet: An Electronic Lexical Database. MIT Press, Cambridge (1998)

    MATH  Google Scholar 

  24. Fill, H.-G., Karagiannis, D.: On the conceptualization of modelling methods using the ADOxx meta modeling platform. Enterp. Model. Inf. Syst. Archit. 8(1), 4–25 (2013)

    Article  Google Scholar 

  25. Fonseca, F., Martin, J.: Learning the differences between ontologies and conceptual schemas through ontology-driven information systems. JAIS – J. Assoc. Inf. Syst. Spec. Issue Ontol. Context IS 8(2), 129–142 (2007)

    Google Scholar 

  26. Fowler, M.: Analysis Patterns: Reusable Object Models. Addison Wesley, Menlo Park (1997)

    Google Scholar 

  27. Gailly F., Poels, G.: Conceptual modeling using domain ontologies: improving the domain-specific quality of conceptual schemas. In: Proceedings of the 10th Workshop on Domain-Specific Modeling, pp. 18–24 (2010)

    Google Scholar 

  28. Gogolla, M., Hohenstein, U.: Towards a semantic view of an extended entity-relationship model. ACM Trans. Database Syst. 16(3), 369–416 (1991)

    Article  MathSciNet  Google Scholar 

  29. Conesa, J., Storey, V., Sugumaran, V.: Usability of upper level ontologies: the case of ResearchCyc. Data Knowl. Eng. 69(4) (2010)

    Google Scholar 

  30. Gnesi, S., Fabbrini, F., Fusani, M., Trentanni, G.: An automatic tool for the analysis of natural language requirements. informe técnico, CNR Information Science and Technology Institute, pp. 53–62 (2004)

    Google Scholar 

  31. Han, T., Purao, S., Storey, V.: Generating large-scale repositories of reusable artifacts for conceptual design of information systems. Decis. Support Syst. 45, 665–680 (2008)

    Article  Google Scholar 

  32. Harmain, M., Gaizauskas, R.: CM-builder: a natural language-based CASE tool for OO analysis. J. Autom. Softw. Eng. 10(2), 157–181 (2003)

    Article  Google Scholar 

  33. Hartmann, S., Link, S.: English sentence structures and EER modeling. In: Proceedings of the 4th Asia-Pacific Conference on Conceptual Modeling (2007)

    Google Scholar 

  34. Hay, D.C.: Data Model Patterns: Conventions of Thought. Dorset House Publishing, New York (1996)

    Google Scholar 

  35. Jarvenpaa, S.L., Machesky, J.J.: Data analysis and learning: an experimental study of data modeling tools. Int. J. Man Mach. Stud. 31(4), 367–391 (1989)

    Article  Google Scholar 

  36. Kim, N., Lee, S., Moon, S.: Formalized entity extraction methodology for changeable business requirements. J. Inf. Sci. Eng. 24, 649–671 (2008)

    Google Scholar 

  37. Kim, Y., March, S.: Comparing data modeling formalisms. Commun. ACM 38(6), 103–115 (1995)

    Article  Google Scholar 

  38. Kop, C., Fliedl, G. Mayr, H.: From natural language requirements to a conceptual model. In: Proceeding of the First International Workshop on Evolution Support for Model-Based Development and Testing (EMDT2010), pp. 67–73 (2010)

    Google Scholar 

  39. Lenat, D.B.: CYC: a large-scale investment in knowledge infrastructure. Commun. ACM 38(11), 33–38 (1995)

    Article  Google Scholar 

  40. Luisa, M., Mariangela, F., Pierluigi, N.I.: Market research for requirements analysis using linguistic tools. Requirements Eng. 9(1), 40–56 (2004)

    Article  Google Scholar 

  41. Mala, A., Uma, V.: Automatic construction of object oriented design models [UML diagrams] from natural language requirements specification. In: Proceedings of the 9th Pacific Rim international conference on Artificial intelligence (PRICAI 2006), pp. 1155–1159 (2006)

    Google Scholar 

  42. Mascardi, V., Cordì, V., Rosso, P.: A comparison of upper ontologies. Technical report DISI-TR-06-2 (2007)

    Google Scholar 

  43. Mich, L., Garigliano, R.: The NL-OOPS project: object oriented modeling using the natural language processing system LOLITA. In: Proceedings of the 4th International Conference on the Applications of Natural Language to Information Systems (NLDB 1999) (1999)

    Google Scholar 

  44. Miller, G.A.: WordNet: a lexical database for English. Commun. ACM 38(11), 39–41 (1995)

    Article  Google Scholar 

  45. Miyoshi, H., Sugiyama, K., Kobayashi, M., Ogino, T.: An overview of the EDR electronic dictionary and the current status of its utilization. In: Proceedings of the 16th International Conference on Computational Linguistics (1996)

    Google Scholar 

  46. Modha, D.S., Ananthanarayanan, R., Esser, S.K., Ndirango, A., Sherbondy, A.J., Singh, R.: Cognitive computing. Commun. ACM 54(8), 62–71 (2011)

    Article  Google Scholar 

  47. Moody, D.L.: Metrics for evaluating the quality of entity relationship models. In: Ling, T.W., Ram, S., Lee, M.L. (eds.) ER 1998. LNCS, vol. 1507, pp. 211–225. Springer, Heidelberg (1998)

    Chapter  Google Scholar 

  48. Moody, D.L., Shanks, G.G.: What makes a good data model? Evaluating the quality of entity relationship models. In: Loucopoulos, P. (ed.) ER 1994. LNCS, vol. 881, pp. 94–111. Springer, Heidelberg (1994)

    Chapter  Google Scholar 

  49. Moody, D.L., Shanks, G.G.: Improving the quality of data models: empirical validation of a quality management framework. Inf. Syst. 28(6), 619–650 (2003)

    Article  MATH  Google Scholar 

  50. Neill, C., Laplante, P.: Requirement engineering: the state of the practice. IEEE Softw. 20(6), 40–45 (2003)

    Article  Google Scholar 

  51. Omar, N,. Hanna, P., Mc Kevitt, P.: Heuristics-based entity-relationship modelling through natural language processing. In: Proceedings of the Fifteenth Irish Conference on Artificial Intelligence and Cognitive Science (AICS-04), pp. 302–313 (2004)

    Google Scholar 

  52. Paek, Y.K., Seo, J., Kim, G.C.: An expert system with case-based reasoning for database schema design. Decis. Support Syst. 18(1), 83–95 (1996)

    Article  Google Scholar 

  53. Parson, J., Saunders, C.: Cognitive heuristics in software engineering: applying and extending anchoring and adjustment to artifact reuse. IEEE Trans. Softw. Eng. 30(12), 873–888 (2004)

    Article  Google Scholar 

  54. Popescu, D., Rugaber, S., Medvidovic, N., Berry, D.M.: Reducing ambiguities in requirements specifications via automatically created object-oriented models. In: Martell, C. (ed.) Monterey Workshop 2007. LNCS, vol. 5320, pp. 103–124. Springer, Heidelberg (2008)

    Google Scholar 

  55. Purao, S., Storey, V.C.: A multi-layered ontology for comparing relationship semantics in conceptual models of databases. J. Appl. Ontol. 1(1), 117–139 (2005)

    Google Scholar 

  56. Simsion, G.: Data Modeling Theory and Practice. Technique Publications, LLC, New York (2007)

    Google Scholar 

  57. Soares, A., Fonseca, F.: Ontology-Driven Information Systems at Development Time. IJCSS – J. Comput. Syst. Signals 8(2) (2007)

    Google Scholar 

  58. Song, I.-Y., Evans, M., Park, E.: A comparative analysis of entity-relationship diagrams. J. Comput. Softw. Eng. 3(4), 427–459 (1995)

    Google Scholar 

  59. Song, I.-Y., Yano, K., Trujillo, J., Lujan-Mora, S.: A taxonomic class modeling methodology for object-oriented analysis. In: Krostige, T.H.J., Siau, K. (eds.) Information Modeling Methods and Methodologies. Advanced Topics in Databases Series, pp. 216–240. Idea Group Publishing, Hershey (2004)

    Google Scholar 

  60. Sprinkle, J., Rumpe, B., Vangheluwe, H., Karsai, G.: Metamodeling - state of the art and research challenges. In: Giese, H., Karsai, G., Lee, E., Rumpe, B., Schätz, B. (eds.) MBEERTS. LNCS, vol. 6100, pp. 57–76. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  61. Storey, V.C.: Classifying and comparing relationships in conceptual modeling. IEEE Trans. Knowl. Data Eng. 17(11), 1–13 (2005)

    Article  Google Scholar 

  62. Storey, V.C.: Understanding semantic relationships. VLDB J. 2, 455–488 (1993)

    Article  Google Scholar 

  63. Storey, V.C., Chiang, R., Goldstein, R., Dey, D., Sundaresan, S.: Database design with common sense business reasoning and learning. ACM Trans. Database Syst. 22(4), 471–512 (1997)

    Article  Google Scholar 

  64. Thalheim, B.: Entity-Relationship Modeling: Foundations of Database Technology. Springer, Berlin (2000)

    Book  MATH  Google Scholar 

  65. Thonggoom, O., Song, I.-Y., An, Y.: EIPW: a knowledge-based database modeling tool. In: Salinesi, C., Pastor, O. (eds.) CAiSE Workshops 2011. LNBIP, vol. 83, pp. 119–133. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  66. Thonggoom, O., Song, I.-Y., An, Y.: Semi-automatic conceptual data modeling using entity and relationship instance repositories. In: Jeusfeld, M., Delcambre, L., Ling, T.-W. (eds.) ER 2011. LNCS, vol. 6998, pp. 219–232. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  67. Topi, H., Ramesh, V.: Human factors research on data modeling: a review of prior research, an extended framework and future research directions. J. Database Manag. 13, 3–15 (2002)

    Article  Google Scholar 

  68. Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann, Los Altos (2005)

    MATH  Google Scholar 

  69. Zeng, Y.: Recursive object model (ROM)-modeling of linguistic information in engineering design. J. Comput. Ind. 59, 612–625 (2008)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Il-Yeol Song .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Song, IY., Zhu, Y., Ceong, H., Thonggoom, O. (2015). Methodologies for Semi-automated Conceptual Data Modeling from Requirements. In: Johannesson, P., Lee, M., Liddle, S., Opdahl, A., Pastor López, Ó. (eds) Conceptual Modeling. ER 2015. Lecture Notes in Computer Science(), vol 9381. Springer, Cham. https://doi.org/10.1007/978-3-319-25264-3_2

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-25264-3_2

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-25263-6

  • Online ISBN: 978-3-319-25264-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics