Aggregate Table-Driven Querying via Navigation Ontologies in Distributed Statistical Databases

  • Yaxin Bi
  • David Bell
  • Joanne Lamb
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2712)


In this paper we describe a query paradigm based on ontologies, aggregate table-driven querying and expansion of QBE. It has two novel features: visually specifying aggregate table queries and table layout in a single process, and providing users with an ontology guide in composing complex analysis tasks as queries. We present the role of the fundamental concept of ontology in the context of the content representation of distributed databases with large numbers of multi-valued attributes, and in query formulation and processing. The methods and techniques developed for representing and manipulating ontologies for query formulation and processing make extensive use of XML and DOM. The core functionalities of content representation, query formulation without prior knowledge about databases, statistical summary and result presentation are integrated into a front-end client within the underpinning MVC architecture, which has been implemented in Java and JAXP.


Content Representation Query Formulation System Client Local Ontology Mission Project 
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.
    Zloof, M.: Query by Example. AFIPS, 44, (1975).Google Scholar
  2. 2.
    Bi, Y., Murtagh, F. and McClean, S.I.: Metadata and XML for Organising and Accessing Multiple Statistical Data Sources, Proceedings of ASC International Conference, Edinburgh, (1999) 393–404.Google Scholar
  3. 3.
    Scotney, B.W., McClean, S.I., Rodgers, M. C.: Optimal and Efficient Integration of Heterogeneous Summary Tables in a Distributed Database. The Journal of Data and Knowledge Engineering, Vol. 29. (1999) 337–350.zbMATHCrossRefGoogle Scholar
  4. 4.
    Sadreddini, M. N. Bell, D. A. and McClean, S. I.: A Model for Integration of Raw Data and Aggregate Views in Heterogeneous Statistical databases. Database Technology, Vol. 4(2), (1992) 115–127.Google Scholar
  5. 5.
    Gamma, E., Helm, R., Johnson, R., and Vlissides, J.: Design Patterns: Elements of Reusable Object-Oriented Software. Addison-Wesley (1994).Google Scholar
  6. 6.
    Tanin, E., Plaisant, C., Shneiderman, B.: Broadening Access to Large Online Databases by Generalizing Query Previews, Proceedings of the Symposium on New Paradigms in Information Visualization and Manipulation, (2000) 80–85.Google Scholar
  7. 7.
    Levy, A. Y., Rajaraman, A., Ordille, J. J.: Querying Heterogeneous Information Sources Using Source Descriptions. Proceedings of the 22nd VLDB Conference, Bombay, India. (1996).Google Scholar
  8. 8.
    Wache, H. Vogele, T. Visser, U. Stuckenschmidt, H. Schuster, G. Neumann, H., H ubner, S.: Ontology-based integration of information — a survey of existing approaches. In Stuckenschmidt, H. (ed.): IJCAI-01 Workshop: Ontologies and Information Sharing, (2001) 108–117.Google Scholar
  9. 9.
    Gruber, T.: A translation Approach to Portable Ontology Specifications. Knowledge Acquisition. Vol. 5(2), (1993)199–220.CrossRefGoogle Scholar
  10. 10.
    McClean, S., Páircéir, R., Scotney, B., Greer, K.: A Negotiation Agent for Distributed Heterogeneous Statistical Databases in SSDBM (2002) 207–217.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Yaxin Bi
    • 1
  • David Bell
    • 1
  • Joanne Lamb
    • 2
  1. 1.School of Computer ScienceThe Queen’s University of BelfastBelfastUK
  2. 2.CESUniversity of EdinburghEdinburghUK

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