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Advanced Operators for Similarity Search

  • Deepak PEmail author
  • Prasad M. Deshpande
Chapter
Part of the SpringerBriefs in Computer Science book series (BRIEFSCOMPUTER)

Abstract

This chapter targets to provide a reasonably comprehensive overview of the various similarity search operators that have been proposed over the last one-and-a-half decades. We consider various advanced operators for similarity search under three heads, (a) those that build upon the weighted sum operation, (b) operators that enhance the basic skyline operator and (c) other modes of similarity search not already covered under the first two heads. We outline each similarity operator by describing the semantics of the operator, followed by a schematic example to illustrate the result set determination under the operator, and end by outlining real-world scenarios that motivate the usage of the operator. We cover more than thirty similarity operators in fair amount of detail in this chapter, forming an extensive overview of the state-of-the-art in similarity operators.

Keywords

Query Point Skyline Query Query Object Neighbor Query Skyline Point 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    C.-Y. Chan, H. Jagadish, K.-L. Tan, A. K. Tung, and Z. Zhang. On high dimensional skylines. In Advances in Database Technology-EDBT 2006, pages 478–495. Springer, 2006.Google Scholar
  2. 2.
    C. L. Clarke, M. Kolla, G. V. Cormack, O. Vechtomova, A. Ashkan, S. Büttcher, and I. MacKinnon. Novelty and diversity in information retrieval evaluation. In Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval, pages 659–666. ACM, 2008.Google Scholar
  3. 3.
    E. Dellis and B. Seeger. Efficient computation of reverse skyline queries. In Proceedings of the 33rd international conference on Very large data bases, pages 291–302. VLDB Endowment, 2007.Google Scholar
  4. 4.
    P. M. Deshpande and D. Padmanabhan. Efficient reverse skyline retrieval with arbitrary nonmetric similarity measures. In EDBT 2011, 14th International Conference on Extending Database Technology, Uppsala, Sweden, March 21-24, 2011, Proceedings, pages 319–330, 2011.Google Scholar
  5. 5.
    T. Emrich, M. Franzke, N. Mamoulis, M. Renz, and A. Züfle. Geo-social skyline queries. In Database Systems for Advanced Applications, pages 77–91. Springer, 2014.Google Scholar
  6. 6.
    H. Ferhatosmanoglu, I. Stanoi, D. Agrawal, and A. El Abbadi. Constrained nearest neighbor queries. In Advances in Spatial and Temporal Databases, pages 257–276. Springer, 2001.Google Scholar
  7. 7.
    D. Fuhry, R. Jin, and D. Zhang. Efficient skyline computation in metric space. In Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology, pages 1042–1051. ACM, 2009.Google Scholar
  8. 8.
    Y. Gao, B. Zheng, G. Chen,W.-C. Lee, K. C. Lee, and Q. Li. Visible reverse k-nearest neighbor queries. In Data Engineering, 2009. ICDE’09. IEEE 25th International Conference on, pages 1203–1206. IEEE, 2009.Google Scholar
  9. 9.
    A. Jain, P. Sarda, and J. R. Haritsa. Providing diversity in k-nearest neighbor query results. In Advances in Knowledge Discovery and Data Mining, pages 404–413. Springer, 2004.Google Scholar
  10. 10.
    W. Jin, J. Han, and M. Ester. Mining thick skylines over large databases. In Knowledge Discovery in Databases: PKDD 2004, pages 255–266. Springer, 2004.Google Scholar
  11. 11.
    F. Korn and S. Muthukrishnan. Influence sets based on reverse nearest neighbor queries. In ACM SIGMOD Record, volume 29, pages 201–212. ACM, 2000.Google Scholar
  12. 12.
    Y. Kumar, R. Janardan, and P. Gupta. Efficient algorithms for reverse proximity query problems. In Proceedings of the 16th ACM SIGSPATIAL international conference on Advances in geographic information systems, page 39. ACM, 2008.Google Scholar
  13. 13.
    C. Li, N. Zhang, N. Hassan, S. Rajasekaran, and G. Das. On skyline groups. In Proceedings of the 21st ACM international conference on Information and knowledge management, pages 2119–2123. ACM, 2012.Google Scholar
  14. 14.
    X. Lian and L. Chen. Similarity search in arbitrary subspaces under l p-norm. In Data Engineering, 2008. ICDE 2008. IEEE 24th International Conference on, pages 317–326. IEEE, 2008.Google Scholar
  15. 15.
    X. Lin, Y. Yuan, Q. Zhang, and Y. Zhang. Selecting stars: The k most representative skyline operator. In Data Engineering, 2007. ICDE 2007. IEEE 23rd International Conference on, pages 86–95. IEEE, 2007.Google Scholar
  16. 16.
    Q. Liu, Y. Gao, G. Chen, Q. Li, and T. Jiang. On efficient reverse k-skyband query processing. In Database Systems for Advanced Applications, pages 544–559. Springer, 2012.Google Scholar
  17. 17.
    S. Nutanong, E. Tanin, and R. Zhang. Visible nearest neighbor queries. In Advances in Databases: Concepts, Systems and Applications, pages 876–883. Springer, 2007.Google Scholar
  18. 18.
    D. Papadias, Y. Tao, G. Fu, and B. Seeger. Progressive skyline computation in database systems. ACM Transactions on Database Systems (TODS), 30(1):41–82, 2005.Google Scholar
  19. 19.
    R. Pereira, A. Agshikar, G. Agarwal, and P. Keni. Range reverse nearest neighbor queries. In KICSS, 2013.Google Scholar
  20. 20.
    M. Sharifzadeh and C. Shahabi. The spatial skyline queries. In Proceedings of the 32nd international conference on Very large data bases, pages 751–762. VLDB Endowment, 2006.Google Scholar
  21. 21.
    Y. Shi and B. Graham. A similarity search approach to solving the multi-query problems. In Computer and Information Science (ICIS), 2012 IEEE/ACIS 11th International Conference on, pages 237–242. IEEE, 2012.Google Scholar
  22. 22.
    Y. Tao, D. Papadias, and X. Lian. Reverse knn search in arbitrary dimensionality. In Proceedings of the Thirtieth international conference on Very large data bases-Volume 30, pages 744–755. VLDB Endowment, 2004.Google Scholar
  23. 23.
    Y. Tao, X. Xiao, and J. Pei. Efficient skyline and top-k retrieval in subspaces. Knowledge and Data Engineering, IEEE Transactions on, 19(8):1072–1088, 2007.Google Scholar
  24. 24.
    A. K. Tung, R. Zhang, N. Koudas, and B. C. Ooi. Similarity search: a matching based approach. In Proceedings of the 32nd international conference on Very large data bases, pages 631–642. VLDB Endowment, 2006.Google Scholar
  25. 25.
    R. Yager and F. Petry. Hypermatching: Similarity matching with extreme values. Fuzzy Systems, IEEE Transactions on, 22(4):949–957, Aug 2014.Google Scholar
  26. 26.
    J. Zhang, D. Papadias, K. Mouratidis, and M. Zhu. Spatial queries in the presence of obstacles. In Advances in Database Technology-EDBT 2004, pages 366–384. Springer, 2004.Google Scholar
  27. 27.
    Z. Zhang, C. Jin, and Q. Kang. Reverse k-ranks query. Proceedings of the VLDB Endowment, 7(10), 2014.Google Scholar

Copyright information

© The Author(s) 2015

Authors and Affiliations

  1. 1.IBM ResearchBangaloreIndia

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