Requirements Engineering

, Volume 12, Issue 4, pp 231–244 | Cite as

Cognitive complexity in data modeling: causes and recommendations

Original Article


Data modeling is a complex task for novice designers. This paper conducts a systematic study of cognitive complexity to reveal important factors pertaining to data modeling. Four major sources of complexity principles are identified: problem solving principles, design principles, information overload, and systems theory. The factors that lead to complexity are listed in each category. Each factor is then applied to the context of data modeling to evaluate if it affects data modeling complexity. Redundant factors from different sources are ignored, and closely linked factors are merged. The factors are then integrated to come up with a comprehensive list of factors. The factors that cannot largely be controlled are dropped from further analysis. The remaining factors are employed to develop a semantic differential scale for assessing cognitive complexity. The paper concludes with implications and recommendations on how to address cognitive complexity caused by data modeling.


Data modeling Cognitive complexity Problem solving Design principles Information overload Systems theory 


  1. 1.
    Topi H, Ramesh V (2002) Human factors research on data modeling: a review of prior research, an extended framework and future research directions. J Database Manag 13:3–15Google Scholar
  2. 2.
    Bock D, Ryan T (1993) Accuracy in modeling with extended entity relationship and OO data models. J Database Manag 4(4):30–39Google Scholar
  3. 3.
    Shoval P, Shiran S (1997) Entity-relationship and object-oriented data modeling—an experimental comparison of design quality. Data Knowl Eng 21:297–315MATHCrossRefGoogle Scholar
  4. 4.
    Batra D, Antony SR (1994) Novice errors in database design. Eur J Inform Syst 3:57–69Google Scholar
  5. 5.
    Tversky A, Kahnemann D (1974) Judgment under uncertainty: heuristics and biases. Science 185:1124–1131CrossRefGoogle Scholar
  6. 6.
    Batra D, Wishart NA (2004) Comparing a rule-based approach with a pattern-based approach under varying task complexity in conceptual data modeling. Int J Hum Comput Interact 61:397–419Google Scholar
  7. 7.
    Hay DC (1996) Data Model Patterns: Conventions Of Thoughts. Dorset House Publishers, New YorkGoogle Scholar
  8. 8.
    Glaser R, Chi MTH (1988) Overview. In: Chi MTH, Glaser R, Farr MJ (eds) The nature of expertise. Erlbaum, Hillsdale, pp xv–xxviiiGoogle Scholar
  9. 9.
    Lord RG, Maher KJ (1991) Cognitive theory in industrial and organizational psychology. In: Dunnette MD, Hough LM (eds) Handbook of industrial and organizational psychology. Consulting Psychologists Press, Palo AltoGoogle Scholar
  10. 10.
    Hardgrave BC, Dalal NP (1995) Comparing object-oriented and extended entity-relationship data modeling. J Database Manag 6:15–21Google Scholar
  11. 11.
    Liao CC, Palvia PC (2000) The impact of data models and task complexity on end-user performance: an experimental investigation. Int J Hum Comput Stud 52:831–845CrossRefGoogle Scholar
  12. 12.
    Shoval P, Evan-Chaime M (1987) Database schema design: an experimental comparison between normalization and information analysis. Data Base 18:39–39Google Scholar
  13. 13.
    Weber R (1996) Are attributes entities? A study of database designers’ memory structures. Inform Syst Res 7:137–162Google Scholar
  14. 14.
    Kieras D, Polson P (1985) An approach to the formal analysis of user complexity. Int J Man Mach Stud 22:365–394Google Scholar
  15. 15.
    Bruner JS, Goodnow JJ, Austin GA (1956) A study of thinking. Wiley, New YorkGoogle Scholar
  16. 16.
    Evans JSBT (1983) Thinking and reasoning: psychological approaches. Routledge & Kegan Paul, BostonGoogle Scholar
  17. 17.
    Casti JL (2001) Complexity. In: Encyclopaedia Britannica OnlineGoogle Scholar
  18. 18.
    Flood RL, Carson ER (1988) Dealing with complexity: an introduction to the theory and application of systems science. Plenum, New YorkGoogle Scholar
  19. 19.
    Niekerk KvK, Buhl H (2004) Introduction: comprehending complexity. In: Niekerk KvK, Buhl H (eds) The significance of complexity: approaching a complex world through science, theology, and the humanities. Ashgate, AldershotGoogle Scholar
  20. 20.
    Miller G (1956) The magical number seven, plus or minus two: some limits on our capacity for processing information. Psychol Rev 63:81–97CrossRefGoogle Scholar
  21. 21.
    Uhr L, Vossier C, Weman J (1962) Pattern recognition over distortions by human subjects and a computer model of human form perception. J Exp Psychol 63:227–234CrossRefGoogle Scholar
  22. 22.
    Pippenger N (1978) Complexity theory. Sci Am 238:114–125CrossRefGoogle Scholar
  23. 23.
    Siau K, Cao Q (2001) Unified modeling language (UML)—a complexity analysis. J Database Manag 12:26–34Google Scholar
  24. 24.
    Siau K, Tian Y (2001) The complexity of unified modeling language—a GOMS analysis. In: 14th international conference on information systems (ICIS’01). New Orleans, LAGoogle Scholar
  25. 25.
    Siau K, Erickson J, Lee LY (2005) Theoretical vs. practical complexity: the case of UML. J Database Manag 16:40–57Google Scholar
  26. 26.
    Zendler A, Pfeiffer T, Eicks M, Lehner F (2001) Experimental comparison of coarse-grained concepts in UML, OML, and TOP. J Syst Software 57:21–30CrossRefGoogle Scholar
  27. 27.
    Rossi M, Brinkkemper S (1996) Complexity metrics for systems development methods and techniques. Inform Syst 21:209–227CrossRefGoogle Scholar
  28. 28.
    Reeves WW (1996) Cognition and complexity: the cognitive science of managing complexity. Scarecrow Press, LanhamGoogle Scholar
  29. 29.
    Reeves WW (1999) Learner-centered design: a cognitive view of managing complexity in product, information, and environmental design. Sage, Thousand OaksGoogle Scholar
  30. 30.
    Polya G (1985) How to solve it. Princeton University Press, PrincetonGoogle Scholar
  31. 31.
    Newell A, Simon HA (1972) Human problem solving. Prentice-Hall, Englewood CliffsGoogle Scholar
  32. 32.
    Funke J (1991) Solving complex problems: exploration and control of complex social problems. In: Sternberg RJ, Frensch PA (eds) Complex problem solving: principles and mechanisms. Erlbaum, Hillsdale, pp 185–222Google Scholar
  33. 33.
    Norman DA, Draper SW (1986) User centered system design: new perspectives on human–computer interaction. Erlbaum, HillsdaleGoogle Scholar
  34. 34.
    Nielsen J, Molich R (1989) Teaching use interface design based on usability engineering. SIGSCHI Bull 21:45–48CrossRefGoogle Scholar
  35. 35.
    Norman DA (1988) The psychology of everyday things. Basic Books, New YorkGoogle Scholar
  36. 36.
    Chen PP (1976) The entity-relationship model—toward a unified view of data. ACM Trans Database Syst 1:9–36CrossRefGoogle Scholar
  37. 37.
    Codd EF (1970) A relational model of data for large shared banks. Commun ACM 13:377–387MATHCrossRefGoogle Scholar
  38. 38.
    Srinivasan A, Te’eni D (1995) Modeling as constrained problem-solving: an empirical study of the data modeling process. Manag Sci 41:419–434MATHCrossRefGoogle Scholar
  39. 39.
    Purao S, Storey V, Han T (2003) Improving reuse-based design: augmenting analysis patterns reuse with learning. Inform Syst Res 14:269–290CrossRefGoogle Scholar
  40. 40.
    Wurman RS (1989) Information anxiety. Doubleday, New YorkGoogle Scholar
  41. 41.
    Luhmann N (1995) Social systems. Stanford University Press, StanfordGoogle Scholar
  42. 42.
    Rosch E (1978) Principles of categorization. In: Rosch E, Lloyd BB (eds) Cognition and categorization. Erlbaum, HillsdaleGoogle Scholar
  43. 43.
    Holland JH, Holyoak KJ, Nisbett RE, Thagard PR (1986) Induction: processes of inference, learning, and discovery. MIT, CambridgeGoogle Scholar
  44. 44.
    Teorey T, Yang D, Fry JF (1986) A logical design methodology for relational databases using the extended entity-relationship model. ACM Comput Surv 18:197–222MATHCrossRefGoogle Scholar
  45. 45.
    Banathy BH (1991) Systems design of education: a journey to create the future. Educational Technology Publications, Englewood CliffsGoogle Scholar
  46. 46.
    Guillemette R (1989) Development and validation of a reader-based documentation measure. Int J Man Mach Stud 30:551–574Google Scholar
  47. 47.
    Stone EF (1978) Research methods in organizational behavior. Scott, Foresman, GlenviewGoogle Scholar
  48. 48.
    Hevner A, March S, Ram S, Park J (2004) Design science research in information systems. MIS Quart 28:75–105Google Scholar
  49. 49.
    Batra D, Sein M (1993) Improving conceptual database design through feedback. Int J Man Mach Stud 40:653–676Google Scholar
  50. 50.
    Wand Y, Weber R (2002) Research commentary: information systems and conceptual modeling—a research agenda. Inform Syst Res 13:363–376CrossRefGoogle Scholar
  51. 51.
    Larman C (2004) Agile and iterative development: a manager’s guide. Addison-Wesley, BostonGoogle Scholar
  52. 52.
    Teorey T, Wei G, Bolton DL, Koenig JA (1989) ER model clustering as an aid for user communication and documentation in database design. Commun ACM 32:975–987CrossRefGoogle Scholar
  53. 53.
    Date CJ (2000) An introduction to database systems. Addison-Wesley, ReadingGoogle Scholar
  54. 54.
    Whitlock W, Nelson K, Rapp R (2003) Modern database management casebook. Prentice-Hall, Upper Saddle RiverGoogle Scholar
  55. 55.
    Batra D, Zanakis SH (1994) A conceptual database design methodology based on rules and heuristics. Eur J Inform Syst 3:57–69Google Scholar
  56. 56.
    Maier D (1988) The theory of relational databases. Computer Science Press, RockvilleGoogle Scholar
  57. 57.
    Antony SR, Batra D (2002) A consulting system for conceptual database design. Database Adv Inform Syst 33:54–68Google Scholar
  58. 58.
    Bak P (1996) How nature works: the science of self-organized criticality. Copernicus, New YorkMATHGoogle Scholar
  59. 59.
    Mayer R (1989) Models for understanding. Rev Educ Res 59:43–64CrossRefGoogle Scholar
  60. 60.
    Abts C (2002) COTS-based systems and make vs. buy decisions. In: International workshop on reuse economics. AustinGoogle Scholar
  61. 61.
    Taeschler D (2002) Metrical approaches to customer equity through CRM. In: Montgomery research CRM Project, Vol 3Google Scholar
  62. 62.
    Soloway E, Pryor A (1996) The next generation in human–computer interaction. Commun ACM 39:16–18CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Limited 2006

Authors and Affiliations

  1. 1.College of Business AdministrationFlorida International UniversityMiamiUSA

Personalised recommendations