Encyclopedia of Database Systems

2018 Edition
| Editors: Ling Liu, M. Tamer Özsu

Top-K Selection Queries on Multimedia Datasets

  • Amélie Marian
Reference work entry
DOI: https://doi.org/10.1007/978-1-4614-8265-9_429

Synonyms

Aggregation algorithms for middleware systems; Evaluation of fuzzy queries over multimedia systems, Ranked multimedia retrieval

Definition

Traditionally, queries over structured (e.g., relational) data identify the exact matches for the queries. This exact-match query model is not appropriate for a multimedia dataset scenario where queries are inherently fuzzy – often expressing user preferences and not hard Boolean constraints – and are best answered with a ranked, or “top-k,” list of the best matching objects. Efficient top-k query algorithms for such applications must take into account the specific challenges in accessing multimedia data. In particular, the query model should consider the access interfaces available to retrieve object attribute information, as well as the cost of retrieving this attribute information.

Historical Background

Content management in multimedia repositories is an important problem as more and more multimedia applications are developed. For...

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Recommended Reading

  1. 1.
    Chang KC-C, Hwang S. Minimal probing: supporting expensive predicates for top-k queries. In: Proceedings of the ACM SIGMOD International Conference on Management of Data; 2002. p. 346–57.Google Scholar
  2. 2.
    Chaudhuri S, Gravano L, Marian A. Optimizing top-k selection queries over multimedia repositories. IEEE Trans Knowl Data Eng. 2004;16(8):992–1009.CrossRefGoogle Scholar
  3. 3.
    Fagin R. Combining fuzzy information from multiple systems. In: Proceedings of the 15th ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems; 1996. p. 216–26.Google Scholar
  4. 4.
    Fagin R, Lotem A, Naor M. Optimal aggregation algorithms for middleware. In: Proceedings of the 20th ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems; 2001. p. 102–13.Google Scholar
  5. 5.
    Fagin R, Lotem A, Naor M. Optimal aggregation algorithms for middleware. J Comput Syst Sci. 2003;66(4):614–56.MathSciNetzbMATHCrossRefGoogle Scholar
  6. 6.
    Güntzer U, Balke W-T, Kießling W Optimizing multi-feature queries for image databases. In: Proceedings of the 26th International Conference on Very Large Bata Bases; 2000. p. 419–28.Google Scholar
  7. 7.
    Hristidis V, Koudas N, Papakonstantinou Y PREFER: a system for the efficient execution of multi-parametric ranked queries. In: Proceedings of the ACM SIGMOD International Conference on Management of Data; 2001. p. 259–70.Google Scholar
  8. 8.
    Marian A, Bruno N, Gravano L. Evaluating top-k queries over web-accessible databases. ACM Trans Database Syst. 2004;29(2):319–62.CrossRefGoogle Scholar
  9. 9.
    Natsev A, Chang Y-C, Smith JR, Li C-S, Vitter JS. Supporting incremental join queries on ranked inputs. In: Proceedings of the 27th International Conference on Very Large Data Bases; 2001. p. 281–90.Google Scholar
  10. 10.
    Nepal S, Ramakrishna MV. Query processing issues in image (multimedia) databases. In: Proceedings of the 15th International Conference on Data Engineering; 1999. p. 22–9.Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Computer Science DepartmentRutgers UniversityNew BrunswickUSA

Section editors and affiliations

  • Jeffrey Xu Yu
    • 1
  1. 1.The Chinese University of Hong KongHong KongChina