HIQM: A Methodology for Information Quality Monitoring, Measurement, and Improvement

  • Cinzia Cappiello
  • Paolo Ficiaro
  • Barbara Pernici
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4231)


Hybrid Information Quality Management (HIQM) methodology is conceived to be a support to solve run-time data quality problems. The analysis of the business processes and context in the design phase allows identifying critical points in the business tasks in which information quality might be improved. In these points, information quality blocks have to be inserted in order to continuously monitor the information flows. Through suitable checks, failures due to information quality problems can be detected. Furthermore, failures and warnings in service execution may depend on a wide variety of causes. Along the causes, the methodology also produces a list of the suitable recovery actions for a timely intervention and quality improvement. The methodology is explained by means of a running example.


Data Quality Business Process Fuzzy Number Quality Dimension Information Quality 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Ardissono, L., Console, L., Goy, A., Petrone, G., Picardi, C., Segnan, M., Theseider Dupre, D.: Advanced fault analysis in web service composition. In: Proceedings of the International World Wide Web Conference (poster session), Chiba, Japan, pp. 1090–1091 (2005)Google Scholar
  2. 2.
    Ballou, D.P., Wang, R.Y., Pazer, H.L., Tayi, G.K.: Modelling information manufacturing systems to determine information product quality. Management Science 44(4), 462–533 (1998)MATHCrossRefGoogle Scholar
  3. 3.
    Chen, C.H.: Fuzzy logic and neural network handbook. McGraw-Hill, New York (1996)MATHGoogle Scholar
  4. 4.
    Chen, S.J., Hwang, C.L.: Fuzzy multiple attribute decision making: Methods and applications. Springer, Heidelberg (1992)MATHGoogle Scholar
  5. 5.
    English, L.P.: Improving data warehouse and business information quality. John Wiley & Sons, Chichester (1999)Google Scholar
  6. 6.
    Redman, T.C.: Data quality for the information age. Artech House (1996)Google Scholar
  7. 7.
    Saaty, T.L.: The analytical hierarchy process. McGraw-Hill, New York (1980)Google Scholar
  8. 8.
    Shankaranarayan, G., Wang, R.Y., Ziad, M.: Modeling the manufacture of an information product with ip-map. In: Proceedings of the 6th International Conference on Information Quality (2000)Google Scholar
  9. 9.
    Wand, Y., Wang, R.Y.: Anchoring data quality dimensions in ontological foundations. Communication of the ACM 39(11) (1996)Google Scholar
  10. 10.
    Wang, R.Y.: A product perspective on total data quality management. Communications of the ACM 41(2) (1998)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Cinzia Cappiello
    • 1
  • Paolo Ficiaro
    • 1
  • Barbara Pernici
    • 1
  1. 1.Dipartimento di Elettronica e InformazionePolitecnico di Milano 

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