Skip to main content
Log in

A proposal for a set of attributes relevant for Web portal data quality

  • Published:
Software Quality Journal Aims and scope Submit manuscript

Abstract

Data Quality is a critical issue in today’s interconnected society. Advances in technology are making the use of the Internet an ever-growing phenomenon and we are witnessing the creation of a great variety of applications such as Web Portals. These applications are important data sources and/or means of accessing information which many people use to make decisions or to carry out tasks. Quality is a very important factor in any software product and also in data. As quality is a wide concept, quality models are usually used to assess the quality of a software product. From the software point of view there is a widely accepted standard proposed by ISO/IEC (the ISO/IEC 9126) which proposes a quality model for software products. However, until now a similar proposal for data quality has not existed. Although we have found some proposals of data quality models, some of them working as “de facto” standards, none of them focus specifically on web portal data quality and the user’s perspective. In this paper, we propose a set of 33 attributes which are relevant for portal data quality. These have been obtained from a revision of literature and a validation process carried out by means of a survey. Although these attributes do not conform to a usable model, we think that it might be considered as a good starting point for constructing one.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Notes

  1. As with much of the research into DQ, in this paper we will use the terms information and data as being synonymous.

References

  • Aboelmeged, M. (2000). A soft system perspective on information quality in electronic commerce. In Fifth Conference on Information Quality (pp. 318–319).

  • Bouzeghoub, M., & Kedad, Z. (2001) Quality in data warehousing. In M. Piattini, C. Calero, & M. Genero (Eds.), Information and Database Quality. Kluwer Academic Publishers.

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

  • Burgess, M., Fiddian, N., & Gray, W. (2004). Quality Measures and The Information Consumer. In Ninth International Conference on Information Quality (pp. 373–388).

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

  • Caro, A., Calero, C., Caballero, I., & Piattini, M. (2005). Data quality in web applications: A state of the art. In P. Isaias & M. B. Nunes (Eds.), IADIS International Conference WWW/Internet 2005 (Vol. 2, pp. 364–368). Lisboa-Portugal.

  • Caro, A., Calero, C., Caballero, I., & Piattini, M. (2006). Defining a data quality model for web portals. In WISE2006, The 7th International Conference on Web Information Systems Engineering (pp. 363–374). Springer LNCS 4255, Wuhan, China.

  • Carver, J., Jaccheri, L., Morasca, S., & Shull, F. (2003). Issues in Using Students in Empirical Studies in Software Engineering Education. In 9th International Software Metrics Symposium (METRICS’03) (239 pp). IEEE Computer Society, Los Alamitos, CA, USA.

  • Collins, H. (2001). Corporate portal definition and features. AMACOM.

  • Eppler, M. (2001). A generic framework for information quality in knowledge-intensive processes. In Proc. sixth international conference on information quality. pp. 329–346.

  • Eppler, M., Algesheimer, R., & Dimpfel, M. (2003). Quality criteria of content-driven websites and their influence on customer satisfaction and loyalty: An empirical test of an information quality framework. In Eighth International Conference on Information Quality (pp. 108–120).

  • Eppler, M., & Muenzenmayer, P. (2002). Measuring information quality in the web context: A survey of state-of-the-art instruments and an aplication methodology. In Seventh International Conference on Information Quality (pp. 187–196).

  • Fugini, M., Mecella, M., Plebani, P., Pernici, B., & Scannapieco, M. (2002). Data Quality in Cooperative Web Information Systems.

  • Gertz, M., Ozsu, T., Saake, G., & Sattler, K.-U. (2004). Report on the dagstuhl seminar “Data Quality on the Web". SIGMOD Record, 33(1), 127–132.

    Google Scholar 

  • Graefe, G. (2003). Incredible Information on the Internet: Biased information provision and a lack of credibility as a cause of insufficient information quality. In Eighth International Conference on Information Quality (pp. 133–146).

  • Höst, M., Regnell, B., & Wohlin, C. (2000). Using students as subjects—a comparative study of students and professionals in lead-time impact assessment. Empirical Software Engineering, 5, 201–214.

    Article  MATH  Google Scholar 

  • Katerattanakul, P., & Siau, K. (1999). Measuring information quality of web sites: Development of an instrument. In 20th International Conference on Information System (pp. 279–285).

  • Katerattanakul, P., & Siau, K. (2001). Information quality in internet commerce desing. In M. Piattini, C. Calero, & M. Genero (Eds.), Information and database quality (pp. 45–56). Kluwer Academic Publishers.

  • Kitchenham, B. (2004). Procedures for Performing Systematic Reviews.

  • Kitchenham, B., & Pfleeger, S. L. (2002a). Principles of survey research part 2: Designing a survey. In SIGSOFT Softw. Eng. Notes (Vol. 27, pp. 18–20). ACM Press.

  • Kitchenham, B., & Pfleeger, S. L. (2002b). Principles of survey research part 4: Questionnaire evaluation. In SIGSOFT Softw. Eng. Notes (Vol. 27, pp. 20–23). ACM Press.

  • Kitchenham, B., & Pfleeger, S. L. (2002c). Principles of survey research: Part 3: Constructing a survey instrument. In SIGSOFT Softw. Eng. Notes (Vol. 27, pp. 20–24). ACM Press.

  • Kitchenham, B., & Pfleeger, S. L. (2002d). Principles of survey research: Part 5: Populations and samples. In SIGSOFT Softw. Eng. Notes (Vol. 27, pp. 17–20). ACM Press.

  • Kitchenham, B., & Pfleeger, S. L. (2003). Principles of survey research part 6: Data analysis. In SIGSOFT Softw. Eng. Notes (Vol. 28, pp. 24–27). ACM Press.

  • Knight, S. A., & Burn, J. M. (2005). developing a framework for assessing information quality on the world wide web. Informing Science Journal, 8, 159–172.

    Google Scholar 

  • Kopcso, D., Pipino, L., & Rybolt, W. (2000). The assesment of web site quality. In Fifth International Conference on Information Quality (pp. 97–108).

  • Krosnick, J. A. (1999). Survey research. Annual Review of Psychology, 50, 537–567.

    Article  Google Scholar 

  • Lee, Y. (2002). AIMQ: A methodology for information quality assessment. Information and Management, 40(2), 133–146.

    Google Scholar 

  • Mahdavi, M., Shepherd, J., & Benatallah, B. (2004). A collaborative approach for caching dynamic data in portal applications. In Proceedings of the fifteenth conference on Australian database (Vol. 27, pp. 181–188).

  • Marchetti, C., Mecella, M., Scannapieco, M., & Virgillito, A. (2003). Enabling data quality notification in cooperative information systems through a web-service based architecture. In Fourth International Conference on Web Information Systems Engineering (pp. 329–332).

  • Melkas, H. (2004). Analyzing information quality in virtual service networks with qualitative interview data. In Ninth International Conference on Information Quality (pp. 74–88).

  • Moraga, M. Á., Calero, C., & Piattini, M. (2006). Comparing different quality models for portals. Online Information Review, 30, 555–568.

    Article  Google Scholar 

  • Moustakis, V., Litos, C., Dalivigas, A., & Tsironis, L. (2004). Website quality assesment criteria. In Ninth International Conference on Information Quality (pp. 59–73).

  • Naumann, F., & Rolker, C. (2000). Assesment methods for information quality criteria. In Fifth International Conference on Information Quality (pp. 148–162).

  • Nelson, R., Todd, P., & Wixom, B. (2005) Antecedents of information and system quality: An empirical examination within the context of data warehouse. Journal of Management Information Systems, 21, 199–235.

    Google Scholar 

  • Pernici, B., & Scannapieco, M. (2002). Data quality in web information systems. In 21st International Conference on Conceptual Modeling (pp. 397–413).

  • Pfleeger, S. L., & Kitchenham, B. (2001). Principles of survey research: Part 1: Turning lemons into lemonade. SIGSOFT Softw. Eng. Notes (Vol. 26, pp. 16–18). ACM Press.

  • Pressman, R. (2001). Software Engineering: A Practitioner’s Approach. 5/e. McGraw-Hill.

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

    Google Scholar 

  • Reeves, C., & Bednar, D. (1994). Defining quality: Alternatives and implications. Academy of Management Review, 19, 419–445.

    Article  Google Scholar 

  • Strong, D., Lee, Y., & Wang, R. (1997). Data quality in context. Communications of the ACM, 40(5), 103–110.

    Google Scholar 

  • Wang, R., & Strong, D. (1996). Beyond accuracy: What data quality means to data consumers. Journal of Management Information Systems, 12, 5–33.

    Google Scholar 

  • Winkler, W. (2004). Methods for evaluating and creating data quality. Information Systems, 29, 531–550.

    Article  MathSciNet  Google Scholar 

  • Wohlin, C., Runeson, P., Höst, M., Ohlson, M., Regnell, B., & A., W. (2000). Experimentation in Software Engineering: An Introduction, Kluwer Academic Publishers, 2000.

  • Yang, Z., Cai, S., Zhou, Z., & Zhou, N. (2004). Development and validation of an instrument to measure user perceived service quality of information presenting Web portals. Information and Management, 42, 575–589.

    Article  Google Scholar 

  • Zhu, Y., & Buchmann, A. (2002). Evaluating and selecting web sources as external information resources of a data warehouse. In 3rd International Conference on Web Information Systems Engineering (pp. 149–160).

Download references

Acknowledgments

This research is part of the following projects: ESFINGE (TIC2006-15175-C05-05) granted by the Dirección General de Investigación del Ministerio de Ciencia y Tecnología (Spain), CALIPSO (TIN20005-24055-E) supported by the Ministerio de Educación y Ciencia (Spain), DIMENSIONS (PBC-05-012-1) supported by FEDER and by the “Consejería de Educación y Ciencia, Junta de Comunidades de Castilla-La Mancha” (Spain) and COMPETISOFT (506AC0287) financed by CYTED.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Angélica Caro.

Appendices

Appendix A

Table 9 Categories of data consumer expectations concerning the DQ on the internet

Appendix B

Table 10

Appendix C. Survey Tool

In this paper we have included an English version of the survey. The original survey was presented in Spanish so that the students could understand it as well as possible.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Caro, A., Calero, C., Caballero, I. et al. A proposal for a set of attributes relevant for Web portal data quality. Software Qual J 16, 513–542 (2008). https://doi.org/10.1007/s11219-008-9046-7

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11219-008-9046-7

Keywords

Navigation