Relational Schema Summarization: A Context-Oriented Approach

  • Marcus Sampaio
  • Jefferson Quesado
  • Samarony Barros
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 186)


Query a database by users unfamiliar with its schema can be a challenging test due mainly to the difficulty of understanding dozens or more of possibly poorly designed inter-linked tables, beyond outdated or missing documentation (usability problem). Such users include database developers and sophisticated users: they may eventually need to acquire detailed knowledge of the schema, and then their ability to do so would be greatly improved if they could start with a simplified, easy-to-read schema. Simplified and easy-toread schemas have been studied within a research direction called database schema summarization [8, 10, 11, 12, 13].


Database Schema Mapping Rule Conceptual Context Logical Context Schema Summarization 
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.


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  1. 1.
    Afrati, F., Chirkova, R.: Selecting and Using Views to Compute Aggregate Queries. In: International Conference on Database Theory, pp. 383–397 (2005)Google Scholar
  2. 2.
    Agrawal, S., Chaudhuri, S., Narasayya, V.: Automated Selection of Materialized Views and Indexes for SQL Databases. In: 26th International Conference on Very Large Databases, pp. 496–505 (2000)Google Scholar
  3. 3.
    Blaha, M., Premerlani, W.: Object-Oriented Modeling and Design for Database Applications. Prentice-Hall (2008)Google Scholar
  4. 4.
    Chakaravarthy, V.T., et al.: Efficiently Linking Text Documents with Relevant Structured Information. In: VLDB 2006, pp. 667–678 (2006)Google Scholar
  5. 5.
    Chaudhuri, S., Krishnamurthy, R., Potaminianos, S., Schim, K.: Optimizing Queries with Materialized Views. In: 11th IEEE International Conference on Data Engineering (IEEE ICDE 2011), pp. 190–200 (1995)Google Scholar
  6. 6.
    Chen, D., Chirkova, R., Sadri, F.: Query Optimization Using Restructured Views: Theory and Experiments. Information Systems 34, 353–370 (2009)CrossRefGoogle Scholar
  7. 7.
    Cunningham, C., Galindo-Legaria, C.A., Graefe, G.: PIVOT and UNPIVOT: Optimization and Execution Strategies in an RDBMS. In: 30th VLDB Conference, pp. 998–1009 (2004)Google Scholar
  8. 8.
    Jagadish, H.V., et al.: Making Database Systems Usable. In: SIGMOD 2007, pp. 13–24 (2007)Google Scholar
  9. 9.
    Roussos, Y., Stavrakas, Y., Pavlaki, V.: Towards a Context-Aware Relational Model. In: Proceeding of the Contextual Representation and Reasoning Workshop of the 5th International and Interdisciplinary Conference on Modeling and Using Context, CONTEXT 2005 (2005)Google Scholar
  10. 10.
    Yang, X., Procopiuc, C.M., Srivastava, D.: Summarizing Relational Databases. In: VLDB 2006 (2009)Google Scholar
  11. 11.
    Yu, C., Jagadish, H.V.: Schema Summarization. In: VLDB 2006 (2006)Google Scholar
  12. 12.
    Yu, C., Jagadish, H.V.: Querying Complex Structured Databases. In: VLDB 2007, pp. 1010–1021 (2007)Google Scholar
  13. 13.
    Wu, W., et al.: Discovering Topical Structures of Databases. In: SIGMOD 2008, pp. 1019–1030 (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Marcus Sampaio
    • 1
  • Jefferson Quesado
    • 1
  • Samarony Barros
    • 1
  1. 1.State University of CearáFortalezaBrazil

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