Managing Quality of Large Set of Conceptual Schemas in Public Administration: Methods and Experiences

  • Carlo Batini
  • Marco Comerio
  • Gianluigi Viscusi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7602)


Information growth asks Public Administrations for an effective control over their information asset. Furthermore, having a global representation of the core concepts of such an asset implies to manage large set of conceptual schemas. At the state of the art, the use of repositories of conceptual schemas aims to provide a structured, global and scalable representation of the core concepts managed in complex large scale information systems. In this paper we discuss several quality properties of repositories, analyzing them within a real, large scale experience.


e-Government Data Governance Quality conceptual modeling repository 


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  1. 1.
    Andersen, K.V., Henriksen, H.Z.: E-government maturity models: Extension of the Layne and Lee model. Government Information Quarterly 23, 236–248 (2006)CrossRefGoogle Scholar
  2. 2.
    Tan, B., Leong, C., Hackney, R.: Achieving and Enhancing E-government Integra-tion: Lessons from the Land Data Hub Project of the Singapore Land Authority. In: ICIS 2011 Proceedings (2011)Google Scholar
  3. 3.
    Lam, W.: Barriers to e-government integration. Journal of Enterprise Information Management 18(5), 511–530 (2005)CrossRefGoogle Scholar
  4. 4.
    Picazo-Vela, S., Gutiérrez-Martinez, I., Luna-Reyes, L.F.: Social media in the public sector: perceived benefits, costs and strategic alternatives. In: Proceedings of the 12th Annual International Digital Government Research Conference (dg.o 2011), pp. 198–203 (2011)Google Scholar
  5. 5.
    Batini, C., Di Battista, G., Santucci, G.: Structuring primitives for a dictionary of entity relationship data schemas. Software Engineering. IEEE Transactions on Software Engineering 19, 344–365 (1993)CrossRefGoogle Scholar
  6. 6.
    Batini, C., Barone, D., Garasi, M.F., Viscusi, G.: Design and Use of ER Repositories: Methodologies and Experiences in eGovernment Initiatives. In: Embley, D.W., Olivé, A., Ram, S. (eds.) ER 2006. LNCS, vol. 4215, pp. 399–412. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  7. 7.
    Batini, C., Scannapieco, M.: Data Quality: Concepts, Methodologies and Techniques. Springer, Heidelberg (2006)zbMATHGoogle Scholar
  8. 8.
    Duchateau, F., Bellahsene, Z.: Measuring the Quality of an Integrated Schema. In: Parsons, J., Saeki, M., Shoval, P., Woo, C., Wand, Y. (eds.) ER 2010. LNCS, vol. 6412, pp. 261–273. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  9. 9.
    Akoka, J., Comyn-Wattiau, I.: Entity-relationship and object-oriented model auto-matic clustering. Data & Knowledge Engineering 20, 87–117 (1996)CrossRefGoogle Scholar
  10. 10.
    Shoval, P., Danoch, R., Balabam, M.: Hierarchical entity-relationship diagrams: the model, method of creation and experimental evaluation. Requirements Engineering 9, 217–228 (2004)CrossRefGoogle Scholar
  11. 11.
    Castano, S., De Antonellis, V.D., Fugini, M.G., Pernici, B.: Conceptual schema analysis: techniques and applications. ACM Trans. Database Syst. 23, 286–333 (1998)CrossRefGoogle Scholar
  12. 12.
    Campbell, L.J., Halpin, T.A., Proper, H.A.: Conceptual schemas with abstractions making flat conceptual schemas more comprehensible. Data & Knowledge Engineering 20, 39–85 (1996)zbMATHCrossRefGoogle Scholar
  13. 13.
    Tavana, M., Joglekar, P., Redmond, M.A.: An automated entity-relationship clustering algorithm for conceptual database design. Information Systems 32, 773–792 (2007)CrossRefGoogle Scholar
  14. 14.
    Simperl, E., Sure, Y.: The Business View: Ontology Engineering Costs. In: Hepp, M., Leenheer, P., Moor, A., Sure, Y., Sheth, A. (eds.) Ontology Management, vol. 7, pp. 207–225. Springer, US (2008)Google Scholar
  15. 15.
    Moody, D.L.: Theoretical and practical issues in evaluating the quality of conceptual models: current state and future directions. Data & Knowledge Engineering 55, 243–276 (2005)CrossRefGoogle Scholar
  16. 16.
    Hepp, M.: Possible Ontologies: How Reality Constrains the Development of Relevant Ontologies. IEEE Internet Computing 11, 90–96 (2007)CrossRefGoogle Scholar
  17. 17.
    Christiaens, S., Leenheer, P., Moor, A., Meersman, R.: Ontologising Competencies in an Interorganisational Setting. In: Hepp, M., Leenheer, P., Moor, A., Sure, Y., Sheth, A. (eds.) Ontology Management, vol. 7, pp. 265–288. Springer, US (2008)Google Scholar
  18. 18.
    Keet, C.M.: Enhancing comprehension of ontologies and conceptual models through abstractions. In: AI*IA 2007: Proceedings of the 10th Congress of the Italian Association for Artificial Intelligence on AI*IA 2007 (2007)Google Scholar
  19. 19.
    Dahchour, M., Pirotte, A., Zimányi, E.: Generic Relationships in Information Modeling. In: Spaccapietra, S. (ed.) Journal on Data Semantics IV. LNCS, vol. 3730, pp. 1–34. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  20. 20.
    Ambite, J.L., et al.: Simplifying data access: The energy data collection project. Computer 34(2), 47–54 (2001)CrossRefGoogle Scholar
  21. 21.
    Castano, S., De Antonellis, V.: Global viewing of heterogeneous data sources. IEEE Transactions on Knowledge and Data Engineering 13, 277–297 (2001), ST-Global viewing of heterogeneous dataGoogle Scholar

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© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Carlo Batini
    • 1
  • Marco Comerio
    • 1
  • Gianluigi Viscusi
    • 1
  1. 1.University of Milano-BicoccaMilanoItaly

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