From Data to Reasoning

  • Przemyslaw GrzegorzewskiEmail author
  • Andrzej Kochanski
Part of the Studies in Systems, Decision and Control book series (SSDC, volume 183)


Data appear at the beginning and at the end of any reasonable modeling. Indeed, data deliver a motivation and a starting point for a model construction. But data are also necessary to validate a resulting model. Data bring information on a considered phenomenon. Gathering information enables to widen our knowledge. But, on the other hand, without some knowledge one would not be able to extract information from data and interpret the received information adequately. Such terms like data and information are widely used in the context of scientific modeling and applications. Although sometimes treated interchangeably they are not synonyms. Another closely related concepts are knowledge, uncertainty, reasoning, etc. The main goal of the present chapter is to discuss and clarify the meaning of the aforementioned notions, to indicate their interrelations and set them in the broad framework of the cognition oriented activity. Finally, three basic types of reasoning used both in science as well as in practice is briefly characterized.


Abduction Data Data quality Deduction Induction Information Knowledge Reasoning Uncertainty Wisdom 


  1. 1.
    Ackoff, R.L.: From data to wisdom. J. Appl. Syst. Anal. 16, 3–9 (1989)Google Scholar
  2. 2.
    Ajdukiewicz, K.: Pragmatic Logic (in Polish). PWN, Warsaw (1974)CrossRefGoogle Scholar
  3. 3.
    Awad, E.M., Ghaziri, H.M.: Knowledge Management. Pearson Education International, Upper Saddle River, NJ (2004)Google Scholar
  4. 4.
    Ballou, D.P., Pazer, H.L.: Modeling data and process quality in multi-input, multi-output information systems. Manage. Sci. 31 (1985)Google Scholar
  5. 5.
    Bandemer, H.: Mathematics of Uncertainty. Springer (2006)Google Scholar
  6. 6.
    Bellinger, G., Castro, D., Mills, A.: Data, information, knowledge, and wisdom. (2004)
  7. 7.
    Cleveland, H.: Information as a resource. The Futurist 34–39 (1982)Google Scholar
  8. 8.
    Cooley, M.: Architecture or Bee?. Hogarth Press, London (1987)Google Scholar
  9. 9.
    Couso, I., Dubois, D.: Statistical reasoning with set-valued information: Ontic vs. epistemic views. Int. J. Approx. Reason. 55, 1502–1518 (2014)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Data management association: the six primary dimensions for data quality assessment. Defining Data Quality Dimensions Report (2016)Google Scholar
  11. 11.
    Dempster, A.P.: Upper and lower probabilities induced by a multivalued mapping. Ann. Math. Stat. 38, 325–339 (1967)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Dempster, A.P.: Upper and lower probability inferences based on a sample from a finite univariate population. Biometrika 54, 515–528 (1967)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Gadomski, A.M.: Meta-ontological assumptions: information, preferences and knowledge universal interrelations (cognitive IPK architecture). ENEA’S paper (1999)Google Scholar
  14. 14.
    Hand, D., Manilla, H. Smith, P.: Principles of Data Mining. MIT Press (2001)Google Scholar
  15. 15.
    Harman, G.: Inference to the best explanation. Philos. Rev. 74, 88–95 (1965)CrossRefGoogle Scholar
  16. 16.
    Hartley, R.V.L.: Transmission of information. Bell Syst. Tech. J. 7, 535–563 (1928)CrossRefGoogle Scholar
  17. 17.
    Josephson, J.R., Josephson, S.G. (eds.): Abductive Inference: Computation, Philosophy, Technology. Cambridge University Press, Cambridge (1994)zbMATHGoogle Scholar
  18. 18.
    Klir, G.J., Wierman, M.J.: Uncertainty-Based Information. Springer (1998)Google Scholar
  19. 19.
    Klir, G.J., Yuan, B.: Fuzzy Sets and Fuzzy Logic: Theory and Applications. Prentice Hall (1995)Google Scholar
  20. 20.
    Kochanski, A.: Data preparation. Comput. Method Mater. Sci. 10, 25–29 (2010)Google Scholar
  21. 21.
    Kreinovich, V., Servin, C.: How to test hypotheses when exact values are replaced by intervals to protect privacy: case of t-tests. Departmental Technical Reports (CS), Paper 892, University of Texas at El Paso (2015)Google Scholar
  22. 22.
    Laudon, K.C.: Data quality and due process in large interorganizational record systems. Commun. ACM 29, 4–11 (1986)CrossRefGoogle Scholar
  23. 23.
    Lindley, D.V.: Understanding Uncertainty. Wiley (2006)Google Scholar
  24. 24.
    Pawlak, Z.: Rough sets. Int. J. Comput. Inf. Sci. 11, 341–356 (1982)CrossRefGoogle Scholar
  25. 25.
    Peirce, C.S.: Collected Works. Harvard University Press, Cambridge (1958)Google Scholar
  26. 26.
    Pipino, L.L., Lee, Y.W., Yang, R.Y.: Data quality assessment. Commun. ACM 45 (2002)Google Scholar
  27. 27.
    Pyle, D.: Data collection, preparation, quality and visualization. In: Nong, Y. (ed.), The Handbook of Data Mining. LEA Inc. (2003)Google Scholar
  28. 28.
    Rao, C.R.: Statistics and Truth. World Scientific Publishing (1997)Google Scholar
  29. 29.
    Redman, T.C.: Data Driven: Profiting from Your Most Important Business Asset. Harvard Business Press (2008)Google Scholar
  30. 30.
    Rowley, J.: The wisdom hierarchy: representations of the DIKW hierarchy. J. Inf. Sci. 33, 163–180 (2007)CrossRefGoogle Scholar
  31. 31.
    Shafer, G.: A Mathematical Theory of Evidence. Princeton University Press (1976)Google Scholar
  32. 32.
    Shannon, C.E.: The mathematical theory of communication. Bell Syst. Tech. J. 27(379–423), 623–656 (1948)MathSciNetCrossRefGoogle Scholar
  33. 33.
    Sugeno, M.: Theory of fuzzy integrals and its applications. Ph.D. Dissertation. Tokyo Institute of Technology, Tokyo (1974)Google Scholar
  34. 34.
    Sugeno, M.: Fuzzy measures and fuzzy integrals: a survey. In: Gupta, M.M., Saridis, G.N., Gaines, B.R. (Eds.), Fuzzy Automata and Decision Processes, North-Holland, 89–102 (1977)Google Scholar
  35. 35.
    Wand, Y., Wang, R.Y.: Anchoring data quality dimensions in ontological foundations. Commun. ACM 39, 86–95 (1996)CrossRefGoogle Scholar
  36. 36.
    Wang, Y.W., Strong, D.M.: Beyond accuracy: what data quality means to data consumers. J. Manage. Inf. Syst. 12, 5–33 (1996)CrossRefGoogle Scholar
  37. 37.
    Zadeh, L.A.: Fuzzy sets. Inf. Control 8, 338–353 (1965)CrossRefGoogle Scholar
  38. 38.
    Zadeh, L.A.: Fuzzy sets as a basis for a theory of possibility. Fuzzy Sets Syst. 1, 3–28 (1978)MathSciNetCrossRefGoogle Scholar
  39. 39.
    Zeleny, M.: Management support systems: towards integrated knowledge management. Human Syst. Manage. 7, 59–70 (1987)Google Scholar
  40. 40.
    Zins, C.: Conceptual approaches for defining data, information and knowledge. J. Am. Soc. Inf. Sci. Technol. 58, 479–493 (2007)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Systems Research Institute, Polish Academy of SciencesWarsawPoland
  2. 2.Faculty of Mathematics and Information ScienceWarsaw University of TechnologyWarsawPoland
  3. 3.Faculty of Production EngineeringWarsaw University of TechnologyWarsawPoland

Personalised recommendations