Skip to main content
SpringerLink
Log in

Flexible integration of multimedia sub-queries with qualitative preferences

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

An Erratum to this article was published on 29 March 2007

Abstract

Complex multimedia queries, aiming to retrieve from large databases those objects that best match the query specification, are usually processed by splitting them into a set of m simpler sub-queries, each dealing with only some of the query features. To determine which are the overall best-matching objects, a rule is then needed to integrate the results of such sub-queries, i.e., how to globally rank the m-dimensional vectors of matching degrees, or partial scores, that objects obtain on the m sub-queries. It is a fact that state-of-the-art approaches all adopt as integration rule a scoring function, such as weighted average, that aggregates the m partial scores into an overall (numerical) similarity score, so that objects can be linearly ordered and only the highest scored ones returned to the user. This choice however forces the system to compromise between the different sub-queries and can easily lead to miss relevant results. In this paper we explore the potentialities of a more general approach, based on the use of qualitative preferences, able to define arbitrary partial (rather than only linear) orders on database objects, so that a larger flexibility is gained in shaping what the user is looking for. For the purpose of efficient evaluation, we propose two integration algorithms able to work with any (monotone) partial order (thus also with scoring functions): MPO, which delivers objects one layer of the partial order at a time, and iMPO, which can incrementally return one object at a time, thus also suitable for processing top k queries. Our analysis demonstrates that using qualitative preferences pays off. In particular, using Skyline and Region-prioritized Skyline preferences for queries on a real image database, we show that the results we get have a precision comparable to that obtainable using scoring functions, yet they are obtained much faster, saving up to about 70% database accesses.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Balke W-T, Güntzer U (2004) Multi-objective query processing for database systems. In: Proceedings of the 30th international conference on very large data bases (VLDB’04), Toronto, Canada, pp 936–947, September

  2. Balke W-T, Güntzer U, Zheng JX (2004) Efficient distributed skylining for web information systems. In: Proc. of the 6th international conference on extending database technology (EDBT’04), Heraklion, Crete, pp 256–273, March

  3. Bartolini I, Ciaccia P, Oria V, Tamer Özsu M (2004) Integrating the result of multimedia queries using qualitative preferences. Technical Report, IEIIT-BO-06-04, IEIIT, April

  4. Bartolini I, Ciaccia P, Patella M (2000) A sound algorithm for region-based image retrieval using an index. In: Proceedings of the 4th international workshop on query processing and multimedia issue in distributed systems (QPMIDS’00), Greenwich, London, UK, pp 930–934, September

  5. Bartolini I, Ciaccia P, Waas F (2001) FeedbackBypass: a new approach to interactive similarity query processing. In: Proceedings of the 27th international conference on very large data bases (VLDB’01), Rome, Italy, pp 201–210, September

  6. Basseville M (1989) Distance measures for signal processing and pattern recognition. Eur J Signal Process 18(4):349–369

    Article  MathSciNet  Google Scholar 

  7. Böhm K, Mlivoncic M, Schek H-J, Weber R (2001) Fast evaluation techniques for complex similarity queries. In: Proceedings of the 27th international conference on very large data bases (VLDB’01), Rome, Italy, pp 211–220, September

  8. Börzsönyi S, Kossmann D, Stocker K (2001) The skyline operator. In: Proceedings of the 17th international conference on data engineering (ICDE’01), Heidelberg, Germany, pp 421–430, April

  9. Bruno N, Gravano L, Marian A (2002) Evaluating top-k queries over web-accessible databases. In: Proceedings of the 18th international conference on data engineering (ICDE’02), San Jose, CA, pp 369–382, February

  10. Canavos GC (1984) Applied probability and statistical methods. Little, Brown & Co., Toronto, Canada

    Google Scholar 

  11. Chomicki J (2002) Querying with intrinsic preferences. In: Proceedings of the 8th international conference on extending database technology (EDBT’02), Prague, Czech Republic, pp 34–51, March

  12. Ciaccia P, Patella M, Zezula P (1998) Processing complex similarity queries with distance-based access methods. In: Proceedings of the 6th international conference on extending database technology (EDBT’98), Valencia, Spain, pp 9–23, March

  13. Cover TM, Thomas JA (1991) Elements of information theory. Wiley, New York

    MATH  Google Scholar 

  14. Fagin R (1996) Combining fuzzy information from multiple systems. In: Proceedings of the 15th ACM SIGACT-SIGMOD-SIGART symposium on principles of database systems (PODS’96), Montreal, Canada, pp 216–226, June

  15. Fagin R, Kumar R, Sivakumar D (2003) Efficient similarity search and classification via rank aggregation. In: Proceedings of the 2003 ACM SIGMOD international conference on management of data (SIGMOD’03), San Diego, CA, pp 301–312, June

  16. Fagin R, Lotem A, Naor M (2001) Optimal aggregation algorithms for middleware. In: Proceedings of the 20th ACM SIGACT-SIGMOD-SIGART symposium on principles of database systems (PODS’01), Santa Barbara, CA, pp 216–226, May

  17. Fishburn PC (1999) Preference structures and their numerical representations. Theor Comp Sci 217(2):359–383

    Article  MATH  MathSciNet  Google Scholar 

  18. Kießling W (2002) Foundations of preferences in database systems. In: Proceedings of the 28th international conference on very large data bases (VLDB’02), Hong Kong, China, pp 311–322, August

  19. Ortega M, Rui Y, Chakrabarti K, Porkaew K, Mehrota S, Huang TS (1998) Supporting ranked boolean similarity queries in MARS. IEEE Trans Knowl Data Eng 10(6):905–925

    Article  Google Scholar 

  20. Porkaew K, Mehrotra S, Ortega M (1999) Query reformulation for content based multimedia retrieval in MARS. In: Proceedings of the international conference on multimedia computing and systems (ICMCS’99), vol 2, Florence, Italy, pp 747–751, June

  21. Rui Y, Huang TS, Ortega M, Mehrota S (1998) Relevance feedback: a power tool for interactive content-based image retrieval. IEEE Trans Circuits Syst Video Technol 8(5):644–655

    Article  Google Scholar 

  22. Torlone R, Ciaccia P (2002) Which are my preferred items? In: AHÕ2002 workshop on recommendation and personalization in eCommerce (RPeCÕ02), Malaga, Spain, pp 1–9, May

Download references

Author information

Authors and Affiliations

  1. DEIS, University of Bologna—IEIIT-BO/CNR, Bologna, Italy

    Ilaria Bartolini & Paolo Ciaccia

  2. Department of Computer Science, NJ Inst. of Technology, Newark, NJ, USA

    Vincent Oria

  3. School of Computer Science, University of Waterloo, Waterloo, ON, Canada

    M. Tamer Özsu

Authors
  1. Ilaria Bartolini
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Paolo Ciaccia
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Vincent Oria
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. M. Tamer Özsu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ilaria Bartolini.

Additional information

Part of this work was performed while this author was visiting NJIT. An erratum to this article can be found at http://dx.doi.org/10.1007/s11042-007-0114-y

Rights and permissions

Reprints and permissions

About this article

Cite this article

Bartolini, I., Ciaccia, P., Oria, V. et al. Flexible integration of multimedia sub-queries with qualitative preferences. Multimed Tools Appl 33, 275–300 (2007). https://doi.org/10.1007/s11042-007-0103-1

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-007-0103-1

Keywords

Search

Navigation