A Semiotic Approach to Investigate Quality Issues of Open Big Data Ecosystems

  • John Krogstie
  • Shang Gao
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 449)


The quality of data models has been investigated since the mid-nineties. In another strand of research, data and information quality has been investigated even longer. Data can also be looked upon as a type of model (on the instance level), as illustrated e.g. in the product models in CAD-systems. We have earlier presented a specialization of the general SEQUAL-framework to be able to evaluate the combined quality of data models and data. In this paper we look in particular on the identified issues of ‘Big Data’. We find on the one hand that the characteristics of quality of big data can be looked upon in the light of the quality levels of the SEQUAL-framework as it is specialized for data quality, and that there are aspects in this framework that are not covered by the existing work on big data. On the other hand, the exercise has resulted in a useful deepening of the generic framework for data quality, and has in this way improved the practical applicability of the SEQUAL-framework when applied to discussing and assessing quality of big data.


Big data data quality Semiotic levels 


  1. 1.
    Aagesen, G., Krogstie, J.: Analysis and design of business processes using BPMN. In: vom Brocke, J., Rosemann, M. (eds.) Handbook on Business Process Management. Springer (2010)Google Scholar
  2. 2.
    Artz, D., Gil, Y.: A survey of trust in computer science and the semantic web. Web Semantics: Science. Services and Agents on the World Wide Web 5(2), 58–71 (2007)CrossRefGoogle Scholar
  3. 3.
    Asif, M., Krogstie, J.: Externalization of User Model in Mobile Services. International Journal of Interactive Mobile Technologies (iJIM) 8(1), 4–9 (2014)CrossRefGoogle Scholar
  4. 4.
    Batini, C., Scannapieco, M.: Data Quality: Concepts, Methodologies and Techniques. Springer (2006)Google Scholar
  5. 5.
    Batini, C., Cappiello, C., Francalanci, C., Maurino, A.: Methodologies for data quality assessment and improvement. ACM Comput. Surv. 41(3) (2009)Google Scholar
  6. 6.
    Beyer, M. A., Laney, D.: The importance of’big data’: a definition. Stamford. Gartner, CT (2012) Google Scholar
  7. 7.
    Biczok, G., Martinez, S.D., Jelle, T., Krogstie, J.: Navigating Mazemap: Indoor human mobility, spatio-logical ties and future potential. PERMODY IEEE (2014)Google Scholar
  8. 8.
    Embury, S.M., Missier, P., Sampaio, S., Greenwood, R.M., Preece, A.D.: Incorporating domain-specific information quality constraints into database queries. Journal of Data and Information Quality (JDIQ) 1(2), 11 (2009)Google Scholar
  9. 9.
    Falkenberg, E.D., Hesse, W., Lindgreen, P., Nilsson, B.E., Oei, J.L.H., Rolland, C., Stamper, R.K., Assche, F.J.M.V., Verrijn-Stuart, A.A., Voss, K.: A Framework of information system concepts - IFIP WG 8.1 Task Group FRISCO (1996)Google Scholar
  10. 10.
    Francalanci, C., Pernici, B.: View integration: A survey of current developments. Technical Report 93-053, Politecnico de Milano, Milan, Italy (1993) Google Scholar
  11. 11.
    Hella, L., Krogstie, J.: A Structured Evaluation to Assess the Reusability of Models of User Profiles. Paper presented at the EMMSAD Hammamet, Tunis, 7-8/6 (2010)Google Scholar
  12. 12.
    Jiang, L., Barone, D., Borgida, A., Mylopoulos, J.: Measuring and Comparing Effectiveness of Data Quality Techniques. In: van Eck, P., Gordijn, J., Wieringa, R. (eds.) CAiSE 2009. LNCS, vol. 5565, pp. 171–185. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  13. 13.
    Krogstie, J.: Using Quality Function Deployment in Software Requirements Specification. Paper presented at the Fifth International Workshop on Requirements Engineering: Foundations for Software Quality (REFSQ 1999), Heidelberg, Germany, June 14-15 (1999)Google Scholar
  14. 14.
    Krogstie, J.: Integrated Goal, Data and Process Modeling: From TEMPORA to Model-Generated Work-Places. In: Johannesson, P., Søderstrøm, E. (eds.) Information Systems Engineering From Data Analysis to Process Networks, pp. 43–65. IGI (2008)Google Scholar
  15. 15.
    Krogstie, J.: Quality of Business Process Models. Proceedings PoEM 2012, Rostock Germany LNBIP (2012)Google Scholar
  16. 16.
    Krogstie, J.: Quality of Conceptual Data Models. Proceedings 14th ICISO, Stockholm Sweden (2013) Google Scholar
  17. 17.
    Krogstie, J.: Model-based development and evolution of information systems: A quality approach. Springer, London (2012)Google Scholar
  18. 18.
    Krogstie, J.: A Semiotic Framework for Data Quality. Proceedings EMMSAD 2013, Valencia, Spain (June 2013)Google Scholar
  19. 19.
    Krogstie, J.: Evaluating Data Quality for Integration of Data Sources. In: Proceedings PoEM 2013, Riga, Latvia, pp. 39–53 (2013)Google Scholar
  20. 20.
    Krogstie, J., Arnesen, S.: Assessing Enterprise ModelingLanguages using a Generic Quality Framework. In: Krogstie, J., Siau, K., Halpin, T. (eds.) Information Modeling Methods and Methodologies. Idea Group Publishing (2004)Google Scholar
  21. 21.
    Krogstie, J., Dalberg, V., Jensen, S.M.: Process modeling value framework. In: Manolopoulos, Y., Filipe, J., Constantopoulos, P., Cordeiro, J. (eds.) Selected papers from 8th International Conference, ICEIS 2006. LNBIP, vol. 3, pp. 309–321. Springer, Heidelberg (2008)Google Scholar
  22. 22.
    Lindland, O.I., Sindre, G., Sølvberg, A.: Understanding Quality in Conceptual Modelling,”. IEEE Software 11(2), 42–49 (1994)CrossRefGoogle Scholar
  23. 23.
    Marr, B.: Big Data (2014),
  24. 24.
    Martin, N., Poulovassillis, A., Wang, J.: A Methodology and Architecture Embedding Quality Assessment in Data Integration. ACM Journal of Data and Information Quality 4(4) (2012)Google Scholar
  25. 25.
    Moody, D.L.: Metrics for Evaluating the Quality of Entity Relationship Models. In: Ling, T.-W., Ram, S., Li Lee, M. (eds.) ER 1998. LNCS, vol. 1507, pp. 211–225. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  26. 26.
    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)CrossRefGoogle Scholar
  27. 27.
    Moody, D.L.: Theoretical and practical issues in evaluating the quality of conceptual models: Current state and future directions. Data and Knowledge Engineering 55, 243–276 (2005)CrossRefGoogle Scholar
  28. 28.
    Nelson, H.J., Poels, G., Genero, M., Piattini, M.: A conceptual modeling quality framework. Software Quality Journal 20, 201–228 (2012)CrossRefGoogle Scholar
  29. 29.
    Nossum, A., Krogstie, J.: Integrated Quality of Models and Quality of Maps. In: Halpin, T., Krogstie, J., Nurcan, S., Proper, E., Schmidt, R., Soffer, P., Ukor, R. (eds.) Enterprise, Business-Process and Information Systems Modeling. LNBIP, vol. 29, pp. 264–276. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  30. 30.
    Price, R., Shanks, G.: A Semiotic Information Quality Framework. In: IFIP WG8.3 International Conference on DecisionSupport Systems (DSS 2004), Prato, Italy, 1-3, pp. 658–672 (2004)Google Scholar
  31. 31.
    Price, R., Shanks, G.: A semiotic information quality framework: Development and comparative analysis. Journal of Information Technology 20(2), 88–102 (2005)CrossRefGoogle Scholar
  32. 32.
    Shekhar, S., Xiong, H.: Encyclopedia of GIS. Springer (2008)Google Scholar
  33. 33.
    Shneiderman, B.: Designing the User Interface: Strategies for Effective Human- Computer Interaction, 2nd edn. Addison Wesley, Reading (1992)Google Scholar
  34. 34.
    Wad, C.: QoS: Quality Driven Data Abstraction for Large Databases. Worcester Polytechnic Institute (2008)Google Scholar
  35. 35.
    Ware, C.: Information Visualization. Morgan Kaufmann (2000)Google Scholar

Copyright information

© IFIP International Federation for Information Processing 2015

Authors and Affiliations

  • John Krogstie
    • 1
  • Shang Gao
    • 1
  1. 1.Norwegian University of Science and Technology (NTNU)Norway

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