A Probabilistic Approach to Web Portal's Data Quality Evaluation

Advances in technology and the use of the Internet have favoured the emergence of a large number of Web applications, including Web Portals. Web portals provide the means to obtain a large amount of information therefore it is crucial that the information provided is of high quality. In recent years, several research projects have investigated Web Data Quality; however none has focused on data quality within the context of Web Portals. Therefore, the contribution of this research is to provide a framework centred on the point of view of data consumers, and that uses a probabilistic approach for Web portal's data quality evaluation. This paper shows the definition of operational model, based in our previous work.

[1]  Thomas C. Redman,et al.  Data Quality: The Field Guide , 2001 .

[2]  Michael Gertz,et al.  Report on the Dagstuhl Seminar , 2004, SGMD.

[3]  Diane M. Strong,et al.  Data quality in context , 1997, CACM.

[4]  Oscar Nierstrasz,et al.  On the effectiveness of clone detection by string matching , 2006, J. Softw. Maintenance Res. Pract..

[5]  Arie van Deursen,et al.  Source-based software risk assessment , 2003, International Conference on Software Maintenance, 2003. ICSM 2003. Proceedings..

[6]  Michael W. Godfrey,et al.  "Cloning Considered Harmful" Considered Harmful , 2006, 2006 13th Working Conference on Reverse Engineering.

[7]  Verónika Peralta,et al.  A framework for analysis of data freshness , 2004, IQIS '04.

[8]  Massimo Mecella,et al.  Data Quality in Cooperative Information Systems , 2005 .

[9]  Gernot Gräfe,et al.  Incredible Information on the Internet: Biased Information Provision and a Lack of Credibility as a Cause of Insufficient Information Quality , 2003, ICIQ.

[10]  Adir Even,et al.  Enhancing Decision Making with Process Metadata: Theoretical Framework, Research Tool, and Exploratory Examination , 2006, Proceedings of the 39th Annual Hawaii International Conference on System Sciences (HICSS'06).

[11]  Robert Lagerström,et al.  Extended Influence Diagrams for System Quality Analysis , 2007, J. Softw..

[12]  M. Shepperd,et al.  A critique of cyclomatic complexity as a software metric , 1988, Softw. Eng. J..

[13]  Paul W. Oman,et al.  Using metrics to evaluate software system maintainability , 1994, Computer.

[14]  Brenda S. Baker,et al.  On finding duplication and near-duplication in large software systems , 1995, Proceedings of 2nd Working Conference on Reverse Engineering.

[15]  Zheng Zhou,et al.  Development and validation of an instrument to measure user perceived service quality of information presenting Web portals , 2005, Inf. Manag..

[16]  Kostas Kontogiannis,et al.  Evaluation experiments on the detection of programming patterns using software metrics , 1997, Proceedings of the Fourth Working Conference on Reverse Engineering.

[17]  Mario Piattini,et al.  Defining a Data Quality Model for Web Portals , 2006, WISE.

[18]  Helinä Melkas.  Analyzing Information Quality In Virtual Service Networks With Qualitative Interview Data , 2004, ICIQ.

[19]  Mikhaila Burgess,et al.  Quality Measures and The Information Consumer , 2006, ICIQ.

[20]  Martin J. Eppler,et al.  Quality Criteria of Content-Driven Websites and their Influence on Customer Satisfaction and Loyalty: an Empirical Test of an Information Quality Framework , 2003, ICIQ.

[21]  Carol Reeves,et al.  DEFINING QUALITY: ALTERNATIVES AND IMPLICATIONS , 1994 .

[22]  C. Jones,et al.  Software metrics: good, bad and missing , 1994, Computer.

[23]  Houari A. Sahraoui,et al.  Modeling Web-Based Applications Quality: A Probabilistic Approach , 2006, WISE.

[24]  Martin J. Eppler.  Managing Information Quality , 2003 .

[25]  Enrique Herrera-Viedma,et al.  Evaluating the information quality of Web sites: A methodology based on fuzzy computing with words , 2006, J. Assoc. Inf. Sci. Technol..

[26]  Felix Naumann,et al.  Assessment Methods for Information Quality Criteria , 2000, IQ.

[27]  Judea Pearl,et al.  Probabilistic reasoning in intelligent systems - networks of plausible inference , 1991, Morgan Kaufmann series in representation and reasoning.

[28]  P. Oman,et al.  Metrics for assessing a software system's maintainability , 1992, Proceedings Conference on Software Maintenance 1992.

[29]  Diane M. Strong,et al.  Beyond Accuracy: What Data Quality Means to Data Consumers , 1996, J. Manag. Inf. Syst..

[30]  Norman E. Fenton,et al.  Automated population of causal models for improved software risk assessment , 2005, ASE '05.

[31]  Barbara A. Kitchenham,et al.  The use and usefulness of the ISO/IEC 9126 quality standard , 2005, 2005 International Symposium on Empirical Software Engineering, 2005..

[32]  Chiara Francalanci,et al.  Data quality assessment from the user's perspective , 2004, IQIS '04.

[33]  S. Lauritzen.  The EM algorithm for graphical association models with missing data , 1995 .

[34]  Mario Piattini,et al.  Comparing different quality models for portals , 2006, Online Inf. Rev..

[35]  Ettore Merlo,et al.  Experiment on the automatic detection of function clones in a software system using metrics , 1996, 1996 Proceedings of International Conference on Software Maintenance.

[36]  Subhasish Dasgupta,et al.  User Satisfaction with Web Portals: An Empirical Study , 2005 .

[37]  Antonio Vallecillo,et al.  An Ontology for Software Measurement , 2006, Ontologies for Software Engineering and Software Technology.

[38]  Anas N. Al-Rabadi,et al.  A comparison of modified reconstructability analysis and Ashenhurst‐Curtis decomposition of Boolean functions , 2004 .

[39]  Paul W. Oman,et al.  Construction and testing of polynomials predicting software maintainability , 1994, J. Syst. Softw..

[40]  Norman E. Fenton,et al.  Software metrics: roadmap , 2000, ICSE '00.

[41]  Keng Siau,et al.  Measuring information quality of web sites: development of an instrument , 1999, ICIS.

[42]  Martin Neil,et al.  Building large-scale Bayesian networks , 2000, The Knowledge Engineering Review.

[43]  Jens Krinke,et al.  Identifying similar code with program dependence graphs , 2001, Proceedings Eighth Working Conference on Reverse Engineering.