Ronald Fagin
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Abstract
Assume that each object in a database has m grades, or scores, one for each of m attributes. For example, an object can have a color grade, that tells how red it is, and a shape grade, that tells how round it is. For each attribute, there is a sorted list, which lists each object and its grade under that attribute, sorted by grade (highest grade first). Each object is assigned an overall grade, that is obtained by combining the attribute grades using a fixed monotone aggregation function, or combining rule, such as min or average. In this overview, we discuss and compare algorithms for determining the top k objects, that is, k objects with the highest overall grades.
- {BGM02} N. Bruno, L. Gravano, and A. Marian, Evaluating top-k queries over web-accessible databases. In Proceedings of the 18th International Conference on Data Engineering. IEEE Computer Society, 2002. Google Scholar
Digital Library
- {CG96} S. Chaudhuri and L. Gravano. Optimizing queries over multimedia repositories. In Proc. ACM SIGMOD Conference, pages 91-102, 1996. Google ScholarDigital Library
- {Fag99} R. Fagin. Combining fuzzy information from multiple systems. J. Comput. System Sci., 58:83-99, 1999. Google ScholarDigital Library
- {FLN02} R. Fagin, A. Lotem, and M. Naor. Optimal aggregation algorithms for middleware. J. Comput. System Sci., 2002. To appear. Extended abstract appears in Proc. ACM Symp. on Principles of Database Systems, 2001, pp. 102-113. Google ScholarDigital Library
- {GBK00} U. Güntzer, W-T. Balke, and W. Kiessling. Optimizing multi-feature queries in image databases. In Proc. 26th Very Large Databases (VLDB) Conference, pages 419-428, 2000. Google ScholarDigital Library
- {GBK01} U. Güntzer, W-T. Balke, and W. Kiessling. Towards efficient multi-feature queries in heterogeneous environments. In Proc. of the IEEE International Conference on Information Technology: Coding and Computing (ITCC 2001), Las Vegas, USA, April 2001. Google ScholarDigital Library
- {NBE+93} W. Niblack, R. Barber, W. Equitz, M. Flickner, E. Glasman, D. Petkovic, and P. Yanker. The QBIC project: Querying images by content using color, texture and shape. In SPIE Conference on Storage and Retrieval for Image and Video Databases, volume 1908, pages 173-187, 1993. QBIC Web server is http://wwwqbic.almaden.ibm.com/.Google ScholarCross Ref
- {NCS+01} A. Natsev, Y-C. Chang, J. R. Smith, C-S. Li, and J. S. Vitter. Supporting incremental join queries on ranked inputs. In Proc. 27th Very Large Databases (VLDB) Conference, pages 281-290, Rome, Italy, 2001. Google ScholarDigital Library
- {NR99} S. Nepal and M. V. Ramakrishna. Query processing issues in image (multimedia) databases. In Proc. 15th International Conference on Data Engineering (ICDE), pages 22-29, March 1999. Google ScholarDigital Library
- {WHRB99} E. L. Wimmers, L. M. Haas, M. Tork Roth, and C. Braendli. Using Fagin's algorithm for merging ranked results in multimedia middleware. In Fourth IFCIS International Conference on Cooperative Information Systems, pages 267-278. IEEE Computer Society Press, September 1999. Google ScholarDigital Library
- {Zad69} L. A. Zadeh. Fuzzy sets. Information and Control, 8:338-353, 1969.Google ScholarCross Ref
- {Zim96} H. J. Zimmermann. Fuzzy Set Theory. Kluwer Academic Publishers, Boston, 3rd edition, 1996.Google Scholar
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