Multimedia Tools and Applications

, Volume 33, Issue 3, pp 275–300 | Cite as

Flexible integration of multimedia sub-queries with qualitative preferences

  • Ilaria Bartolini
  • Paolo Ciaccia
  • Vincent Oria
  • M. Tamer Özsu
Article

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.

Keywords

Multimedia data integration Qualitative preferences Strict partial orders Skyline Incremental evaluation 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 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, SeptemberGoogle Scholar
  2. 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, MarchGoogle Scholar
  3. 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, AprilGoogle Scholar
  4. 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, SeptemberGoogle Scholar
  5. 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, SeptemberGoogle Scholar
  6. 6.
    Basseville M (1989) Distance measures for signal processing and pattern recognition. Eur J Signal Process 18(4):349–369CrossRefMathSciNetGoogle Scholar
  7. 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, SeptemberGoogle Scholar
  8. 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, AprilGoogle Scholar
  9. 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, FebruaryGoogle Scholar
  10. 10.
    Canavos GC (1984) Applied probability and statistical methods. Little, Brown & Co., Toronto, CanadaGoogle Scholar
  11. 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, MarchGoogle Scholar
  12. 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, MarchGoogle Scholar
  13. 13.
    Cover TM, Thomas JA (1991) Elements of information theory. Wiley, New YorkMATHGoogle Scholar
  14. 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, JuneGoogle Scholar
  15. 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, JuneGoogle Scholar
  16. 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, MayGoogle Scholar
  17. 17.
    Fishburn PC (1999) Preference structures and their numerical representations. Theor Comp Sci 217(2):359–383MATHCrossRefMathSciNetGoogle Scholar
  18. 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, AugustGoogle Scholar
  19. 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):905–925CrossRefGoogle Scholar
  20. 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, JuneGoogle Scholar
  21. 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):644–655CrossRefGoogle Scholar
  22. 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, MayGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2007

Authors and Affiliations

  • Ilaria Bartolini
    • 1
  • Paolo Ciaccia
    • 1
  • Vincent Oria
    • 2
  • M. Tamer Özsu
    • 3
  1. 1.DEISUniversity of Bologna—IEIIT-BO/CNRBolognaItaly
  2. 2.Department of Computer ScienceNJ Inst. of TechnologyNewarkUSA
  3. 3.School of Computer ScienceUniversity of WaterlooWaterlooCanada

Personalised recommendations