By Hans-Jörg Schek (auth.), Christian S. Jensen, Simonas à altenis, Keith G. Jeffery, Jaroslav Pokorny, Elisa Bertino, Klemens Böhn, Matthias Jarke (eds.)

ISBN-10: 3540433244

ISBN-13: 9783540433248

ISBN-10: 354045876X

ISBN-13: 9783540458760

This publication constitutes the refereed complaints of the eighth overseas convention on Extending Database know-how, EDBT 2002, held in Prague, Czech Republic, in March 2002.
The 36 revised complete papers provided including six commercial and alertness papers, thirteen software program demos and one invited paper have been rigorously reviewed and chosen from a complete of 207 submissions. The papers are prepared in topical sections on question transformation, information mining, XML, complex question processing, relocating items, disbursed information, dispensed processing, complicated querying, XML-advanced querying, basic question companies, estimation/histograms, and aggregation.

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Extra resources for Advances in Database Technology — EDBT 2002: 8th International Conference on Extending Database Technology Prague, Czech Republic, March 25–27, 2002 Proceedings

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M. Flickner, Harpreet Sawhney, Wayne Niblack, and Jonathan Ashley. Query by Image and Video Content: The QBIC System. IEEE Computer, 28(9):23–32, September 1995. 11. Norbert Fuhr. Logical and conceptual models for the integration of information retrieval and database systems. 1996. 12. Yoshiharu Ishikawa, Ravishankar Subramanya, and Christos Faloutsos. Mindreader: Querying databases through multiple examples. In Int’l Conf. on Very Large Data Bases, 1998. 13. Carlo Meghini. Fourth DELOS Workshop – Image Indexing and Retrieval.

18 Michael Ortega-Binderberger, Kaushik Chakrabarti, and Sharad Mehrotra The rest of this paper is developed as follows. Section 2 presents some background. Section 3 presents our query refinement framework, and section 4 presents some specific refinement algorithms. Section 5 presents some experiments and an example query refinement application. Section 6 presents related work. Section 7 offers some conclusions. 2 Preliminaries — Similarity Retrieval Model Before we embark on an explanation of query refinement, we first discuss the similarity retrieval model on which we build our query refinement work.

5. Precision–recall graphs for several iterations, and refinement strategies predicates but with the default weights and parameters, notice how the query slowly improves. Figure 5d) starts the query with the pollution profile only, but allowing predicate addition. The predicate on location is added after the first iteration resulting in much better results. Similarly figure 5e) starts only with the location predicate. The initial query execution yields very low results, but the pollution predicate is added after the initial query resulting in a marked improvement.

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Advances in Database Technology — EDBT 2002: 8th International Conference on Extending Database Technology Prague, Czech Republic, March 25–27, 2002 Proceedings by Hans-Jörg Schek (auth.), Christian S. Jensen, Simonas à altenis, Keith G. Jeffery, Jaroslav Pokorny, Elisa Bertino, Klemens Böhn, Matthias Jarke (eds.)


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