Advertisement

Aggregate k Nearest Neighbor Queries in Metric Spaces

  • Xin Ding
  • Yuanliang Zhang
  • Lu Chen
  • Keyu Yang
  • Yunjun GaoEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10988)

Abstract

Aggregate k nearest neighbor (AkNN) queries are useful in many areas, such as multimedia retrieval and resource allocation, to name but a few. Most of existing works on AkNN query only focus on Euclidean space or specific metric space, which employ properties of particular data to accelerate the query. However, due to the complex data types involved and the needs for flexible similarity criteria seen in real applications, properties of particular data cannot be used for general case. Hence, in this paper, we investigate AkNN search in metric spaces, termed as metric AkNN (MAkNN) search, as metric spaces can support any type of data and flexible similarity criteria as long as satisfying triangle inequality. To efficiently answer MAkNN queries, we develop several pruning techniques and corresponding algorithms based on SPB-tree. Extensive experiments using three real data sets verify the efficiency of our MAkNN algorithms.

Keywords

Metric space Aggregate k nearest neighbor query Algorithm 

Notes

Acknowledgments

This work was supported in part by the 973 Program of China under Grant No. 2015CB352502, the NSFC under Grant No. 61522208, the NSFC-Zhejiang Joint Fund under Grant No. U1609217, and the ZJU-Hikvision Joint Project.

References

  1. 1.
    Kalantari, I., McDonald, G.: A data structure and an algorithm for the nearest point problem. IEEE Trans. Softw. Eng. 9(5), 631–634 (1983)CrossRefGoogle Scholar
  2. 2.
    Uhlmann, J.K.: Satisfying general proximity/similarity queries with metric trees. Inf. Process. Lett. 40(4), 175–179 (1991)CrossRefGoogle Scholar
  3. 3.
    Brin, S.: Near neighbor search in large metric spaces. In: VLDB, pp. 574–584 (1995)Google Scholar
  4. 4.
    Navarro, G.: Searching in metric spaces by spatial approximation. VLDB J. 11(1), 28–46 (2002)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Ciaccia, P., Patella, M., Zezula, P.: M-tree: an efficient access method for similarity search in metric spaces. In: VLDB, pp. 426–435 (1997)Google Scholar
  6. 6.
    Dohnal, V., Gennaro, C., Savino, P., Zezula, P.: D-index: distance searching index for metric data sets. Multimed. Tools Appl. 21(1), 9–33 (2003)CrossRefGoogle Scholar
  7. 7.
    Chavez, E., Navarro, G.: A compact space decomposition for effective metric indexing. Pattern Recogn. Lett. 26(9), 1363–1376 (2005)CrossRefGoogle Scholar
  8. 8.
    Almeida, J., Torres, R.D.S., Leite, N.J.: BP-tree: an efficient index for similarity search in high-dimensional metric spaces. In: CIKM, pp. 1365–1368 (2010)Google Scholar
  9. 9.
    Mico, L., Oncina, J., Carrasco, R.C.: A fast branch & bound nearest neighbour classifier in metric spaces. Pattern Recogn. Lett. 17(7), 731–739 (1996)CrossRefGoogle Scholar
  10. 10.
    Ruiz, G., Santoyo, F., Chavez, E., Figueroa, K., Tellez, E.S.: Extreme pivots for faster metric indexes. In: SISAP, pp. 115–126 (2013)CrossRefGoogle Scholar
  11. 11.
    Burkhard, W., Keller, R.: Some approaches to best-match file searching. Commun. ACM 16(4), 230–236 (1973)CrossRefGoogle Scholar
  12. 12.
    Baeza-Yates, R.A., Cunto, W., Manber, U., Wu, S.: Proximity matching using fixed-queries trees. In: CPM, pp. 198–212 (1994)CrossRefGoogle Scholar
  13. 13.
    Bozkaya, T., Ozsoyoglu, M.: Distance-based indexing for high-dimensional metric spaces. In: SIGMOD, pp. 357–368 (1997)CrossRefGoogle Scholar
  14. 14.
    Traina Jr., C., Filho, R.F.S., Traina, A.J.M., Vieira, M.R., Faloutsos, C.: The Omni-family of all-purpose access methods: asimple and effective way to make similarity search more efficient. VLDB J. 16(4), 483–505 (2007)CrossRefGoogle Scholar
  15. 15.
    Ares, L.G., Brisaboa, N.R., Esteller, M.F., Pedreira, O., Places, A.S.: Optimal pivots to minimize the index size for metric access methods. In: SISAP, pp. 74–80 (2009)Google Scholar
  16. 16.
    Chavez, E., Navarro, G., Baeza-Yates, R.A., Marroquin, J.L.: Searching in metric spaces. ACM Comput. Surv. 33, 273–321 (2001)CrossRefGoogle Scholar
  17. 17.
    Mosko, J., Lokoc, J., Skopal, T.: Clustered pivot tables for I/O-optimized similarity search. In: SISAP, pp. 17–24 (2011)Google Scholar
  18. 18.
    Skopal, T., Pokorny, J., Snasel, V.: PM-tree: pivoting metric tree for similarity search in multimedia databases. In: ADBIS, pp. 803–815 (2004)Google Scholar
  19. 19.
    Novak, D., Batko, M., Zezula, P.: Metric index: an efficient and scalable solution for precise and approximate similarity search. Inf. Syst. 36(4), 721–733 (2011)CrossRefGoogle Scholar
  20. 20.
    Chen, L., Gao, Y., Li, X., Jensen, C.S., Chen, G.: Efficient metric indexing for similarity search. In: ICDE (2015, to appear)Google Scholar
  21. 21.
    Papadias, D., Tao, Y., Mouratidis, K., Hui, C.K.: Aggregate nearest neighbor queries in spatial databases. ACM Trans. Database Syst. (TODS) 30(2), 529–576 (2005)CrossRefGoogle Scholar
  22. 22.
    Li, F., Yi, K., Tao, Y., Yao, B., Li, Y., Xie, D., Wang, M.: Exact and approximate flexible aggregate similarity search. VLDB J. 25(3), 317–338 (2016)CrossRefGoogle Scholar
  23. 23.
    Wang, H., Zheng, K., Su, H., Wang, J., Sadiq, S., Zhou, X.: Efficient aggregate farthest neighbour query processing on road networks. In: Wang, H., Sharaf, M.A. (eds.) ADC 2014. LNCS, vol. 8506, pp. 13–25. Springer, Cham (2014).  https://doi.org/10.1007/978-3-319-08608-8_2CrossRefGoogle Scholar
  24. 24.
    Liu, Z., Wang, C., Wang, J.: Aggregate nearest neighbor queries in uncertain graphs. World Wide Web 17(1), 161–188 (2014)CrossRefGoogle Scholar
  25. 25.
    Abbasifard, M.R., Naderi, H., Fallahnejad, Z., Alamdari, O.I.: Approximate aggregate nearest neighbor search on moving objects trajectories. J. Central South Univ. 22(11), 4246–4253 (2015)CrossRefGoogle Scholar
  26. 26.
    Razente, H.L., Barioni, M.C.N., Traina, A.J.M., Traina Jr., C.: Constrained aggregate similarity queries in metric spaces. In: SBBD, pp. 145–159 (2007)Google Scholar
  27. 27.
    Razente, H.L., Barioni, M.C.N., Traina, A.J.M., Faloutsos, C., Traina Jr., C.: A novel optimization approach to efficiently process aggregate similarity queries in metric access methods. In: CIKM, pp. 193–202. ACM (2008)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Xin Ding
    • 1
  • Yuanliang Zhang
    • 1
  • Lu Chen
    • 2
  • Keyu Yang
    • 1
  • Yunjun Gao
    • 1
    Email author
  1. 1.College of Computer ScienceZhejiang UniversityHangzhouChina
  2. 2.Department of Computer ScienceAalborg UniversityAalborgDenmark

Personalised recommendations