Abstract
Proximity searching consists in retrieving the most similar objects to a given query from a database. To do so, the usual approach consists in using an index in order to improve the response time of online queries. Recently, the permutation based algorithms (PBA) were presented, and from then on, this technique has been very successful. In its core, the PBA uses a metric between permutations, typically Spearman Footrule or Spearman Rho. Until now, several proposals based on the PBA have been developed and all of them uses one of those metrics. In this paper, we present a new family of dissimilarity measures between permutations. According to our experimental evaluation, we can reduce up to 30% the original technique costs, while preserving its exceptional answer quality. Since our dissimilarity measures can be applied in any state-of-the-art PBA variant, the impact of our proposal is significant for the similarity search community.
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References
Amato, G., Esuli, A., Falchi, F.: A comparison of pivot selection techniques for permutation-based indexing. Inf. Syst. 52, 176–188 (2015). https://doi.org/10.1016/j.is.2015.01.010
Amato, G., Savino, P.: Approximate similarity search in metric spaces using inverted files. In: 3rd International ICST Conference on Scalable Information Systems, INFOSCALE 2008, Vico Equense, Italy, 4–6 June 2008. p. 28. ICST/ACM (2008). https://doi.org/10.4108/ICST.INFOSCALE2008.3486
Chávez, E., Figueroa, K., Navarro, G.: Proximity searching in high dimensional spaces with a proximity preserving order. In: Gelbukh, A., de Albornoz, Á., Terashima-Marín, H. (eds.) MICAI 2005. LNCS (LNAI), vol. 3789, pp. 405–414. Springer, Heidelberg (2005). https://doi.org/10.1007/11579427_41
Chávez, E., Figueroa, K., Navarro, G.: Effective proximity retrieval by ordering permutations. IEEE Trans. Pattern Anal. Mach. Intell. (TPAMI) 30(9), 1647–1658 (2009)
Chávez, E., Navarro, G.: A probabilistic spell for the curse of dimensionality. In: Buchsbaum, A.L., Snoeyink, J. (eds.) ALENEX 2001. LNCS, vol. 2153, pp. 147–160. Springer, Heidelberg (2001). https://doi.org/10.1007/3-540-44808-X_12
Chávez, E., Navarro, G., Baeza-Yates, R., Marroquín, J.: Proximity searching in metric spaces. ACM Comput. Surv. 33(3), 273–321 (2001)
Diaconis, P., Graham, R.L.: Spearman’s footrule as a measure of disarray. J. R. Stat. Soc. Ser. B (Methodol.) 39(2), 262–268 (1977)
Esuli, A.: Use of permutation prefixes for efficient and scalable approximate similarity search. Inf. Process. Manage. 48(5), 889–902 (2012). https://doi.org/10.1016/j.ipm.2010.11.011
Fagin, R., Kumar, R., Sivakumar, D.: Comparing top k lists. SIAM J. Discrete Math. 17(1), 134–160 (2003)
Figueroa, K., Paredes, R.: List of clustered permutations for proximity searching. In: Brisaboa, N., Pedreira, O., Zezula, P. (eds.) SISAP 2013. LNCS, vol. 8199, pp. 50–58. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-41062-8_6
Figueroa, K., Navarro, G., Chávez, E.: Metric spaces library (2007). http://www.sisap.org/Metric_Space_Library.html
Figueroa, K., Paredes, R., Camarena-Ibarrola, J.A., Reyes, N.: Fixed height queries tree permutation index for proximity searching. In: Carrasco-Ochoa, J.A., Martínez-Trinidad, J.F., Olvera-López, J.A. (eds.) MCPR 2017. LNCS, vol. 10267, pp. 74–83. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-59226-8_8
Hjaltason, G., Samet, H.: Index-driven similarity search in metric spaces. ACM Trans. Database Syst. 28(4), 517–580 (2003). https://doi.org/10.1145/958942.958948
Mohamed, H., Marchand-Maillet, S.: Quantized ranking for permutation-based indexing. Inf. Syst. 52, 163–175 (2015). https://doi.org/10.1016/j.is.2015.01.009
Naidan, B., Boytsov, L., Nyberg, E.: Permutation search methods are efficient, yet faster search is possible. Proc. VLDB Endow. 8(12), 1618–1629 (2015). https://doi.org/10.14778/2824032.2824059
Patella, M., Ciaccia, P.: Approximate similarity search: a multi-faceted problem. J. Discret Algorithms 7(1), 36–48 (2009). https://doi.org/10.1016/j.jda.2008.09.014. Selected papers from the 1st International Workshop on Similarity Search and Applications (SISAP)
Samet, H.: Foundations of Multidimensional and Metric Data Structures (The Morgan Kaufmann Series in Computer Graphics and Geometric Modeling). Morgan Kaufmann Publishers Inc., San Francisco (2005)
Skopal, T.: On fast non-metric similarity search by metric access methods. In: Ioannidi, Y., et al. (eds.) EDBT 2006. LNCS, vol. 3896, pp. 718–736. Springer, Heidelberg (2006). https://doi.org/10.1007/11687238_43
Tellez, E.S., Chavez, E., Navarro, G.: Succint nearest neighbor search. Inf. Syst. 38(7), 1019–1030 (2013)
Zezula, P., Amato, G., Dohnal, V., Batko, M.: Similarity Search: The Metric Space Approach, Advances in Database Systems, vol. 32. Springer, Heidelberg (2006). https://doi.org/10.1007/0-387-29151-2
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Figueroa, K., Paredes, R., Reyes, N. (2018). New Permutation Dissimilarity Measures for Proximity Searching. In: Marchand-Maillet, S., Silva, Y., Chávez, E. (eds) Similarity Search and Applications. SISAP 2018. Lecture Notes in Computer Science(), vol 11223. Springer, Cham. https://doi.org/10.1007/978-3-030-02224-2_10
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