A Framework for Data Quality Aware Query Systems

  • Naiem K. Yeganeh
  • Mohamed A. Sharaf
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6637)


Data Quality (DQ) is increasingly gaining more importance as organizations as well as individuals are relying on data available from various data sources. User satisfaction from query result is directly related to the quality of data returned to user. In this paper we present a framework for DQ aware query systems focused on three key requirements of profiling DQ, capturing user preferences on DQ and processing data quality aware queries.


Analytical Hierarchy Process User Preference Query Result Query Planning Simple Additive Weighting 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Batini, C., Scannapieco, M.: Data Quality: Concepts, Methodologies and Techniques (Data-Centric Systems and Applications). Springer-Verlag New York, Inc., Secaucus (2006)zbMATHGoogle Scholar
  2. 2.
    Benjelloun, O., Garcia-Molina, H., Su, Q., Widom, J.: Swoosh: A generic approach to entity resolution. VLDB Journal (2008)Google Scholar
  3. 3.
    Bohannon, P., Wenfei, F., Geerts, F., Xibei, J., Kementsietsidis, A.: Conditional functional dependencies for data cleaning. In: ICDE (2007)Google Scholar
  4. 4.
    Chomicki, J.: Querying with Intrinsic Preferences. In: Jensen, C.S., Jeffery, K., Pokorný, J., Šaltenis, S., Hwang, J., Böhm, K., Jarke, M. (eds.) EDBT 2002. LNCS, vol. 2287, p. 34. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  5. 5.
    Doan, A., Levy, A.Y.: Efficiently ordering query plans for data integration. In: ICDE (2002)Google Scholar
  6. 6.
    Friedman, T., Bitterer, A.: Magic Quadrant for Data Quality Tools. Gartner Group (2006)Google Scholar
  7. 7.
    Govindarajan, K., Jayaraman, B., Mantha, S.: Preference Queries in Deductive Databases. New Generation Computing (2000)Google Scholar
  8. 8.
    Gravano, L., Ipeirotis, P.G., Jagadish, H.V., Koudas, N., Muthukrishnan, S., Srivastava, D.: Approximate String Joins in a Database (Almost) for Free. In: VLDB (2001)Google Scholar
  9. 9.
    Gravano, L., Ipeirotis, P.G., Koudas, N., Srivastava, D.: Text Joins for Data Cleansing and Integration in an RDBMS. In: ICDE (2003)Google Scholar
  10. 10.
    Hey, J.D.: Do Rational People Make Mistakes? Foundations of Social Sciences, Economics and Ethics (1998)Google Scholar
  11. 11.
    Hwang, C.L., Yoon, K.: Lecture Notes in Economics and Mathematical Systems: Multiple Attribute Decision Making: Methods and Appllication. Springer, Heidelberg (1981)Google Scholar
  12. 12.
    Khatri, H., Fan, J., Chen, Y., Kambhampati, S.: Qpiad: Query processing over incomplete autonomous databases. In: ICDE (2007)Google Scholar
  13. 13.
    Kießling, W.: Foundations of preferences in database systems. In: VLDB (2002)Google Scholar
  14. 14.
    Lacroix, M., Lavency, P.: Preferences: Putting More Knowledge into Queries. In: VLDB (1987)Google Scholar
  15. 15.
    Lakshmanan, L.V.S., Leone, N., Ross, R., Subrahmanian, V.S.: ProbView: a flexible probabilistic database system. ACM TODS (1997)Google Scholar
  16. 16.
    Naumann, F.: Quality-Driven Query Answering for Integrated Information Systems. LNCS, vol. 2261. Springer, Heidelberg (2002)zbMATHGoogle Scholar
  17. 17.
    Naumann, F., Leser, U., Freytag, J.C.: Quality-driven integration of heterogenous information systems. In: VLDB (1999)Google Scholar
  18. 18.
    Nie, Z., Kambhampati, S.: Joint optimization of cost and coverage of query plans in data integration. In: CIKM (2001)Google Scholar
  19. 19.
    Qu, H., Labrinidis, A.: Preference-aware query and update scheduling in web-databases. In: ICDE (2007)Google Scholar
  20. 20.
    Saaty, T.L.: How to Make a Decision: The Analytic Hierarchy Process. European Journal of Operational Research (1990)Google Scholar
  21. 21.
    Saaty, T.L.: Multicriteria Decision Making: The Analytic Hierarchy Process: Planning, Priority Setting, Resource Allocation. RWS Publications (1996)Google Scholar
  22. 22.
    Scannapieco, M., Missier, P., Batini, C.: Data quality at a glance. Datenbank-Spektrum (2005)Google Scholar
  23. 23.
    Simmhan, Y.L., Plale, B., Gannon, D.: A Survey of Data Provenance in e-Science. SIGMOD RECORD (2005)Google Scholar
  24. 24.
    Stonebraker, M., Devine, R., Kornacker, M., Litwin, W., Pfeffer, A., Sah, A., Staelin, C.: An economic paradigm for query processing and data migration in Mariposa. In: Proceedings of the Third International Conference on Parallel and Distributed Information Systems 1994 (2002)Google Scholar
  25. 25.
    Wang, R.Y., Storey, V.C., Firth, C.P.: A framework for analysis of data quality research. IEEE Transactions on Knowledge and Data Engineering(1995)Google Scholar
  26. 26.
    Wang, R.Y., Strong, D.M.: Beyond accuracy: what data quality means to data consumers. Journal of Management Information Systems (1996)Google Scholar
  27. 27.
    Yeganeh, N.K., Sadiq, S.: Avoiding Inconsistency in User Preferences for Data Quality Aware Queries. In: Abramowicz, W., Tolksdorf, R. (eds.) BIS 2010. LNBIP, vol. 47, pp. 59–70. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  28. 28.
    Yeganeh, N., Sadiq, S., Deng, K., Zhou, X.: Data Quality Aware Queries in Collaborative Information Systems. In: Li, Q., Feng, L., Pei, J., Wang, S.X., Zhou, X., Zhu, Q.-M. (eds.) APWeb/WAIM 2009. LNCS, vol. 5446, pp. 39–50. Springer, Heidelberg (2009)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Naiem K. Yeganeh
    • 1
  • Mohamed A. Sharaf
    • 1
  1. 1.School of Information Technology and Electrical EngineeringThe University of QueenslandSt Lucia.Australia

Personalised recommendations