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An Applicable Data Quality Model for Web Portal Data Consumers


Web portals have emerged as an important means by which to access data on the worldwide. The people that use these applications need to ensure that the data recovered is suitable for the task at hand. That is, they need to know the level of quality of the data obtained. This paper introduces the PoDQA (Portal Data Quality Assessment) tool which implements PDQM, a Portal Data Quality Model, which is centered upon the data consumer perspective. Thus, the measurement of data quality is carried out by using the point of view of data consumers. Our work aims to fill the lack of specific proposals for the DQ evaluation in Web portals and tools that put these proposals into practice. The paper illustrate how PoDQA tool works and how it can be used by data consumers in order to, for example, discover the data quality of a specific web portal. PoDQA also suggests several corrective maintenance activities for users who are interested in the improvement of the data quality of their Web portals.

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  1. Angeles, P., MacKinnon, L.: Detection and resolution of data inconsistences, and data integration using data quality criteria. In: Proceedings of the QUATIC'2004, pp. 87–93 (2004)

  2. Bouzeghoub, M., Peralta, V.: A framework for analysis of data freshness. In: Proceedings of the International Workshop on Information Quality in Information Systems (IQIS2004), pp. 59–67. ACM, Paris, France (2004)

  3. Cappiello, C., Francalanci, C., Pernici, B.: Data quality assessment from the user’s perspective. In: Proceedings of the International Workshop on Information Quality in Information Systems (IQIS2004), pp. 68–73. ACM, Paris, Francia (2004)

  4. Caro, A., Calero, C., Caballero, I., Piattini, M.: Defining a Data Quality Model for Web Portals. In: Proceedings of the WISE2006, The 7th International Conference on Web Information Systems Engineering, pp. 363–374. Springer LNCS 4255, Wuhan, China (2006)

  5. Caro, A., Calero, C., Piattini, M.: Development process of the operational version of PDQM. In: Proceedings of the WISE2007, The 8th International Conference on Web Information Systems Engineering pp. 436–448. Springer LNCS, Nancy, Francia (2007)

  6. Collins, H.: Corporate Portal Definition and Features. AMACOM (2001)

  7. Eppler, M.: Managing Information Quality: Increasing the Value of Information in Knowledge-intensive Products and Processes. Springer, New York (2003)

    Google Scholar 

  8. Eppler, M., Algesheimer, R., Dimpfel, M.: Quality Criteria of Content-Driven Websites and Their Influence on Customer Satisfaction and Loyalty: An Empirical Test of an Information Quality Framework. In: Proceedings of the Eighth International Conference on Information Quality, pp. 108–120 (2003)

  9. Eppler, M., Muenzenmayer, P.: Measuring Information Quality in the Web Context: A Survey of State-of-the-Art Instruments and an Application Methodology. In: Proceedings of the Seventh International Conference on Information Quality, pp. 187–196 (2002)

  10. Finkelstein, C., Aiken, P.: XML and Corporate Portals. Access in (1999)

  11. Fugini, M., Mecella, M., Plebani P., Pernici B., Scannapieco M.: Data Quality in Cooperative Web Information Systems. Personal Communication. (2002)

  12. Gertz, M., Ozsu, T., Saake, G., Sattler, K.U.: Report on the Dagstuhl Seminar “Data Quality on the Web”. SIGMOD Rec. 33(1), 127–132 (2004)

    Article  Google Scholar 

  13. Ginige, A., Murugesan, S.: Web Engineering: An Introduction. IEEE Multimed 8(1), 14–17 (2001)

    Article  Google Scholar 

  14. Graefe, G.: Incredible Information on the Internet: Biased Information Provision and a Lack of Credibility as a Cause of Insufficient Information Quality. In: Proceedings of the 8th International Conference on Information Quality, pp. 133–146 (2003)

  15. Ivory, M., Rashmi, S., Marti, H.: Empirically Validated Web Page Design Metrics. In: Proceedings of the SIG-CHI on Human factors in computing systems (SIGCHI'01), pp. 53–60. Seattle, WA, USA (2001)

  16. Katerattanakul, P., Siau, K.: Information quality in internet commerce desing. In: PiattiniCaleroGenero, M.C.M. (ed.) Information and Database Quality, pp. 45–56. Kluwer Academic, Dordrecht (2001)

    Google Scholar 

  17. Katerattanakul, P., Siau, K.: Measuring Information Quality of Web Sites: Development of an Instrument. In: Proceedings of the 20th International Conference on Information System, pp. 279–285 (1999)

  18. Knight, S.A., Burn, J.M.: Developing a Framework for Assessing Information Quality on the World Wide Web. Inf. Sc. J. 8, 159–172 (2005)

    Google Scholar 

  19. Lee, Y.: AIMQ: a methodology for information quality assessment. Inf. Manage. 40, 133–146 (2002), Elsevier Science

    Article  Google Scholar 

  20. Malak, G., Sahraoui, H., Badri, L., Badri, M.: Modeling web-based applications quality: a probabilistic approach. In: Proceedings of the 7th International Conference on Web Information Systems Engineering, pp. 398–404. Springer LNCS, Wuhan, China (2006)

  21. Melkas, H.: Analyzing Information Quality in Virtual service Networks with Qualitative Interview Data. In: Proceedings of the Ninth International Conference on Information Quality, pp. 74–88 (2004)

  22. Moraga, M.Á., Calero, C., Piattini, M.: Comparing different quality models for portals. Online Inf. Rev. 30(5), 555–568 (2006)

    Article  Google Scholar 

  23. Moustakis, V., Litos, C., Dalivigas, A., Tsironis, L.: Website Quality Assessment Criteria. In: Proceedings of the Ninth International Conference on Information Quality, pp. 59–73 (2004)

  24. Naumann, F., Rolker, C.: Assesment Methods for Information Quality Criteria. In: Proceedings of the Fifth International Conference on Information Quality, pp. 148–162 (2000)

  25. Neil, M., Fenton, N.E., Nielsen, L.: Building large-scale Bayesian Networks. Knowl. Eng. Rev. 15(3), 257–284 (2000)

    Article  MATH  Google Scholar 

  26. Nielsen, J.: Designing Web Usability: The Practice of Simplicity. New Riders, Thousand Oaks, CA (2000)

    Google Scholar 

  27. Pernici, B., Scannapieco, M.: Data Quality in Web Information Systems. In: Proceedings of the 21st International Conference on Conceptual Modeling, pp. 397–413 (2002)

  28. Pipino, L., Lee, Y., Wang, R.: Data quality assessment. Commun. ACM. 45(4), 211–218 (2002)

    Article  Google Scholar 

  29. Pressman, R.: Software Engineering: a Practitioner's Approach, 5th edn. McGraw-Hill, New York (2001)

    Google Scholar 

  30. Redman, T.: Data quality: The field guide. Digital, Boston (2000)

    Google Scholar 

  31. Sahraou,i H., Boukadoum, M., Chawiche, H.M., Mai, G., Serhani, M.A.: A fuzzy logic framework to improve the performance and interpretation of rule-based quality prediction models for object-oriented software. In Proceedings of the 26th Computer Software and Applications Conference (COMPSAC02) (2002)

  32. Shanks, G., Corbitt, B.: Understanding Data Quality: Social and Cultural Aspects. In: Proceedings of the 10th Australasian Conference on Information Systems, pp. 785–797. Wellington, New Zealand (1999)

  33. Strong, D., Lee, Y., Wang, R.: Data quality in context. Commun. ACM. 40(5), 103–110 (1997)

    Article  Google Scholar 

  34. Wang, R., Strong, D.: Beyond accuracy: What data quality means to data consumers. J. Manage. Inf. Syst. 12(4), 5–33 (1996) Armonk; Spring

    MATH  Google Scholar 

  35. Winkler, W.: Methods for evaluating and creating data quality. Inf. Syst. 29(7), 531–550 (2004)

    Article  MathSciNet  Google Scholar 

  36. Xiao, L., Dasgupta, S.: User Satisfaction with Web Portals: An empirical Study. In: Gao, Y. (eds.) Web Systems Design and Online Consumer Behavior, pp. 193–205. Idea Group, Hershey, PA (2005)

    Google Scholar 

  37. Yang, Z., Cai, S., Zhou, Z., Zhou, N.: Development and validation of an instrument to measure user perceived service quality of information presenting Web portals. Inf. Manage. 42, 575–589 (2004), Elsevier Science

    Article  Google Scholar 

  38. Zhu, Y., Buchmann, A.: Evaluating and Selecting Web Sources as external Information Resources of a Data Warehouse. In: Proceedings of the 3rd International Conference on Web Information Systems Engineering, pp. 149–160 (2002)

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Correspondence to Coral Calero.

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Calero, C., Caro, A. & Piattini, M. An Applicable Data Quality Model for Web Portal Data Consumers. World Wide Web 11, 465–484 (2008).

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  • web portals
  • data quality
  • data quality model
  • data quality assessment
  • users
  • developers