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
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Notes
- 1.
Words is available at http://icon.shef.ac.uk/Moby/.
- 2.
Color is available at http://www.sisap.org/Metric_Space_Library.html.
- 3.
DNA is available at http://www.ncbi.nlm.nih.gov/genome.
References
Kalantari, I., McDonald, G.: A data structure and an algorithm for the nearest point problem. IEEE Trans. Softw. Eng. 9(5), 631–634 (1983)
Uhlmann, J.K.: Satisfying general proximity/similarity queries with metric trees. Inf. Process. Lett. 40(4), 175–179 (1991)
Brin, S.: Near neighbor search in large metric spaces. In: VLDB, pp. 574–584 (1995)
Navarro, G.: Searching in metric spaces by spatial approximation. VLDB J. 11(1), 28–46 (2002)
Ciaccia, P., Patella, M., Zezula, P.: M-tree: an efficient access method for similarity search in metric spaces. In: VLDB, pp. 426–435 (1997)
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)
Chavez, E., Navarro, G.: A compact space decomposition for effective metric indexing. Pattern Recogn. Lett. 26(9), 1363–1376 (2005)
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)
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)
Ruiz, G., Santoyo, F., Chavez, E., Figueroa, K., Tellez, E.S.: Extreme pivots for faster metric indexes. In: SISAP, pp. 115–126 (2013)
Burkhard, W., Keller, R.: Some approaches to best-match file searching. Commun. ACM 16(4), 230–236 (1973)
Baeza-Yates, R.A., Cunto, W., Manber, U., Wu, S.: Proximity matching using fixed-queries trees. In: CPM, pp. 198–212 (1994)
Bozkaya, T., Ozsoyoglu, M.: Distance-based indexing for high-dimensional metric spaces. In: SIGMOD, pp. 357–368 (1997)
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)
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)
Chavez, E., Navarro, G., Baeza-Yates, R.A., Marroquin, J.L.: Searching in metric spaces. ACM Comput. Surv. 33, 273–321 (2001)
Mosko, J., Lokoc, J., Skopal, T.: Clustered pivot tables for I/O-optimized similarity search. In: SISAP, pp. 17–24 (2011)
Skopal, T., Pokorny, J., Snasel, V.: PM-tree: pivoting metric tree for similarity search in multimedia databases. In: ADBIS, pp. 803–815 (2004)
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)
Chen, L., Gao, Y., Li, X., Jensen, C.S., Chen, G.: Efficient metric indexing for similarity search. In: ICDE (2015, to appear)
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)
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)
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_2
Liu, Z., Wang, C., Wang, J.: Aggregate nearest neighbor queries in uncertain graphs. World Wide Web 17(1), 161–188 (2014)
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)
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)
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)
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.
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Ding, X., Zhang, Y., Chen, L., Yang, K., Gao, Y. (2018). Aggregate k Nearest Neighbor Queries in Metric Spaces. In: Cai, Y., Ishikawa, Y., Xu, J. (eds) Web and Big Data. APWeb-WAIM 2018. Lecture Notes in Computer Science(), vol 10988. Springer, Cham. https://doi.org/10.1007/978-3-319-96893-3_24
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