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Mauricio Espitia Triana

FUNDAMENTOS DE QUÍMICA ECOLÓGICA.

1. La Comunicación Química entre insectos.

In this thesis, a compact-trie-based k-nearest-neighbor search method is proposed. Through the k-nearest-neighbor search performance comparison against the brute-force based and the k-d tree based methods, the compact-trie-based method did show consistent performance superiority. The intrinsic relationship between the grid partitioning and the Cartesian coordinates of spatial points composed of positive decimal numbers, and the natural space partitioning by a compact trie constructed from the modified Cartesian coordinates of spatial points composed of positive decimal numbers inspired me to devise the compact-trie-based method.

In this pilot project so far, it has been shown that the compact-trie-based method performed better than the brute-force based and the k-d tree based methods in tests, searching the k nearest neighbors for a given two-dimensional spatial point within one million two-dimensional spatial points. More theoretic work needs to be done to prove the better performance of the compact-trie-based method in theory. And the k-nearest- neighbor search performance comparison result needs to be verified with more robust tests and using well-established and well-recognized standard libraries.

It is worth mentioning that the different search schemes employed in the compact- trie-based method and the k-d tree based method may have a significant role responsible for the performance difference. It would be ideal if the performance comparison was carried out while the two methods employed the same search scheme. Applying the best- first search scheme to the k-d tree based method seems promising, though maintaining the priority queue for the non-terminal nodes traversed seems not computationally cost-

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effective at the first sight because the k-d tree is a binary tree and any non-terminal node of the k-d tree has two and only two child nodes. Applying the best-first search scheme to the compact-trie-based method seems relatively not that promising because for each non- terminal node traversed, a list of its child nodes sorted by their minimum distances to the query point may need to be created, which could be computationally expensive. It might be quite worthwhile to implement both and find out how they actually turn out.

So far, the implementation of the compact-trie-based method has limited the input to positive decimal numbers. Expanding the input to negative decimal numbers may just be a matter of conversion. And other numeral systems might be worthwhile exploring, especially the binary system because any information can be represented by bits or binary numbers, and bit-based storage and/or computing may be more efficient.

Last, it is more significant to explore the potential applicability of the compact- trie-based method to high-dimensional data, to tackle the curse of dimensionality.

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Bibliography

[1] R. E. Bellman. Dynamic programming. Princeton University Press, 1957. [2] E. Fredkin. Trie memory. Communication of the ACM, 3(9):490-499, 1960.

[3] D. R. Morrison. PATRICIA - practical algorithm to retrieve information coded in alphanumeric. Journal of the ACM, 15(4):514-534, 1968.

[4] D. E. Knuth. The art of computer programming volume 3, sorting and searching. Addison-Wesley, 1972.

[5] W. A. Burkhard and R. M. Keller. Some approaches to best-match file searching. Communications of the ACM, 16(4):230-236, 1973.

[6] K. Fukunaga. A branch and bound algorithm for computing k-nearest neighbors. IEEE Transactions on Computers, 24(7):750-753, 1975.

[7] J. H. Friedman, F. Baskett, and L. J. Shustek. An algorithm for finding nearest neighbors. IEEE Transactions on Computers, 24(10):1000-1006, 1975.

[8] J. H. Friedman, J. L. Bentley, and R. A. Finkel. An algorithm for finding best matches in logarithmic expected time. ACM Transactions on Mathematical Software, 3(3):209-226, 1977.

[9] J. L. Bentley, and J. H. Friedman. Data structures for range searching. ACM Computing Surveys, 11(4):397-409, 1979.

[10] J. L. Bentley. Multidimensional divide-and-conquer. Communications of the ACM, 23(4):214-229, 1980.

[11] A. Guttman. R-trees: a dynamic index structure for spatial searching. Proceedings of the 1984 ACM SIGMOD international conference on Management of data, 47- 57, 1984.

[12] J.-I. Aoe. An efficient digital search algorithm by using a double-array structure. IEEE Transaction on Software Engineering, 15(9):1066-1077, 1989.

[13] R. F. Sproull. Refinements to nearest-neighbor searching in k-dimensional trees. Algorithmica, 6:579-589, 1991.

[14] J.-I. Aoe, K. Morimoto, and T. Sato. An efficient implementation of trie structures. Software-Practice and Experience, 22(9):695-721, 1992.

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[15] T. Brinkhoff, H.-P. Kriegel, and B. Seeger. Efficient processing of spatial joins using R-trees. Proceedings of the 1993 ACM SIGMOD international conference on Management of data, 237-246, 1993.

[16] S. Arya, D. M. Mount, N. S. Netanyahu, R. Silverman, A. Y. Wu. An optimal algorithm for approximate nearest neighbor searching. Proceedings of the fifth annual ACM-SIAM symposium on Discrete algorithms, 573-582, 1994.

[17] H. Shang. Trie methods for text and spatial data of secondary storage. Ph.D. Dissertation, School of Computer Science, McGill University, 1995.

[18] N. Roussopoulos, S. Kelley, and F. Vincent. Nearest neighbor queries. Proceedings of the 1995 ACM SIGMOD international conference on management of data, 71-79, 1995.

[19] G. R. Hjaltason, and H. Samet. Ranking in spatial databases. Proceedings of the 4th International Symposium on Advances in Spatial Databases, 83-95, 1995. [20] P. Indyk and R. Motwani. Approximate nearest neighbors: towards removing the

curse of dimensionality. Proceedings of the Symposium on Theory of Computing, 604-613, 1998.

[21] S. Maneewongvatana and D. M. Mount. It’s okay to be skinny, if your friends are fat. Center for Geometric Computing 4th Annual Workshop on Computational Geometry, 1999.

[22] Z. Song and N. Roussopoulos. K-nearest neighbor search for moving query point. Proceedings of the Seventh International Symposium on Advances in Spatial and Temporal Databases, 79-96, 2001.

[23] Y. F. Tao, D. Papadias, and Q. M. Shen. Continuous nearest neighbor search. Proceedings of the 28th international conference on Very Large Data Bases, 287- 298, 2002.

[24] G. R. Hjaltason and H. Samet. Index-driven similarity search in metric spaces. ACM Transactions on Database Systems, 28(4):517-580, 2003.

[25] S. Shekhar and S. Chawla. Spatial databases: a tour. Prentice Hall, 2003.

[26] "Trie." Wikipedia: The Free Encyclopedia. Wikimedia Foundation, Inc. 22 July 2004. Web. 10 Aug. 2004. <https://en.wikipedia.org/wiki/Trie>

[27] X. P. Xiong, M. F. Mokbel, and W. G. Aref. SEA-CNN: scalable processing of continuous k-nearest neighbor queries in spatio-temporal databases. Proceedings of the 21st International Conference on Data Engineering, 643-654, 2005.

57

[28] K. Mouratidis, D. Papadias, and M. Hadjieleftheriou. Conceptual partitioning: an efficient method for continuous nearest neighbor monitoring. Proceedings of the 2005 ACM SIGMOD international conference on Management of data, 634-645, 2005.

[29] N. Askitis and R. Sinha. HAT-trie: a cache-conscious trie-based data structure for strings. Proceedings of the thirtieth Australasian conference on Computer science, 62:97-105, 2007.

[30] M. Moreau and W. Osborn. mqr-tree: a 2-dimensional spatial access method. Journal of Computer Science and Engineering, 15(2):1-12, 2012.

[31] M. Fuketa, H. Kitagawa, T. Ogawa, K.Morita and J.-I. Aoe. Compression of double array structures for fixed length keywords. Information Processing and Management: an International Journal, 50(5):796-806, 2014.

[32] M. R. Abbasifard, B. Ghahremani, and H. Naderi. A survey on nearest neighbor search methods. International Journal of Computer Applications, 95(25):39-52, 2014.

[33] S. Ilarri, E. Mena, and A. Illarramendi. Location-dependent query processing: where we are and where we are heading. ACM Computing Surveys, 42(3):1-73, 2010.

[34] M. Minsky and S. Papert. Perceptrons. MIT Press, Cambridge, Mass., 1969. [35] A. Andoni. Nearest neighbor search: the old, the new, and the impossible. Ph.D.

Dissertation, Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, 2009.

[36] R. Weber, H.-J. Schek, and S. Blott. A quantitative analysis and performance study for similarity-search methods in high-dimensional spaces. Proceedings of the 24th VLDB Conference, 194-205, 1998.