Quality and efficiency in high dimensional Nearest neighbor search

SIGMOD-PODS'09 - Proceedings of the International Conference on Management of Data and 28th Symposium on Principles of Database Systems

Volumn , Issue , 2009, Pages 563-575

  Tao, Yufei ^a   Yi, Ke ^b   Sheng, Cheng ^a   Kalnis, Panos ^c  

^a CHINESE UNIVERSITY OF HONG KONG   (Hong Kong)

^b HONG KONG UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Hong Kong)

^c KING ABDULLAH UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Saudi Arabia)

Author keywords

Locality sensitive hashing; Nearest neighbor search

Indexed keywords

ACCESS METHODS; APPROXIMATE RETRIEVAL; DATA SET SIZE; EFFECTIVE INDEX; HIGH DIMENSIONAL SPACES; HIGH-DIMENSIONAL; LINEAR SPACES; LOCALITY SENSITIVE HASHING; NEAREST NEIGHBOR SEARCH; NEAREST NEIGHBOR SEARCHES; ORDER OF MAGNITUDE; ORDERS OF MAGNITUDE; PRACTICAL SOLUTIONS; QUERY COSTS; QUERY DISTRIBUTIONS; QUERY RESULTS; RELATIONAL DATABASE; STATE OF THE ART;

IMAGE RETRIEVAL; QUALITY FUNCTION DEPLOYMENT; TOTAL QUALITY MANAGEMENT;

DATABASE SYSTEMS;

EID: 70849088674     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1559845.1559905     Document Type: Conference Paper

References (36)

1. A. Andoni and P. Indyk. Near-optimal hashing algorithms for approximate nearest neighbor in high dimensions. In FOCS, pages 459-468, 2006.
2. S. Arya, D. M. Mount, N. S. Netanyahu, R. Silverman, and A. Y. Wu. An optimal algorithm for approximate nearest neighbor searching fixed dimensions. J. ACM, 45(6):891-923, 1998.
3. V. Athitsos, M. Potamias, P. Papapetrou, and G. Kollios. Nearest neighbor retrieval using distance-based hashing. In ICDE, pages 327-336, 2008.
4. M. Bawa, T. Condie, and P. Ganesan. Lsh forest: self-tuning indexes for similarity search. In WWW, pages 651-660, 2005.
5. N. Beckmann, H. Kriegel, R. Schneider, and B. Seeger. The R*-tree: An efficient and robust access method for points and rectangles. In SIGMOD, pages 322-331, 1990.
6. K. P. Bennett, U. Fayyad, and D. Geiger. Density-based indexing for approximate nearest-neighbor queries. In SIGKDD, pages 233-243, 1999.
7. S. Berchtold, C. Bohm, H. V. Jagadish, H.-P. Kriegel, and J. Sander. Independent quantization: An index compression technique for high-dimensional data spaces. In ICDE, pages 577-588, 2000.
8. S. Berchtold, D. A. Keim, H.-P. Kriegel, and T. Seidl. Indexing the solution space: A new technique for nearest neighbor search in high-dimensional space. TKDE, 12(1):45-57, 2000.
9. K. S. Beyer, J. Goldstein, R. Ramakrishnan, and U. Shaft. When is nearest neighbor meaningful? In ICDT, pages 217-235, 1999.
10. C. Bohm. A cost model for query processing in high dimensional data spaces. TODS, 25(2):129-178, 2000.
11. M. M. Breunig, H.-P. Kriegel, R. T. Ng, and J. Sander. Lof: Identifying density-based local outliers. In SIGMOD, pages 93-104, 2000.
12. S. Chaudhuri and L. Gravano. Evaluating top-k selection queries. In VLDB, pages 397-410, 1999.
13. C.-M. Chen and Y. Ling. A sampling-based estimator for top-k query. In ICDE, pages 617-627, 2002.
14. P. Ciaccia and M. Patella. Pac nearest neighbor queries: Approximate and controlled search in high-dimensional and metric spaces. In ICDE, pages 244-255, 2000.
15. M. Datar, N. Immorlica, P. Indyk, and V. S. Mirrokni. Locality-sensitive hashing scheme based on p-stable distributions. In SoCG, pages 253-262, 2004.
16. R. Fagin, R. Kumar, and D. Sivakumar. Efficient similarity search and classification via rank aggregation. In SIGMOD, pages 301-312, 2003.
17. R. Fagin, A. Lotem, and M. Naor. Optimal aggregation algorithms for middleware. In PODS, 2001.
18. H. Ferhatosmanoglu, E. Tuncel, D. Agrawal, and A. E. Abbadi. Approximate nearest neighbor searching in multimedia databases. In ICDE, pages 503-511, 2001.
19. P. Ferragina and R. Grossi. The string b-tree: a new data structure for string search in external memory and its applications. J. ACM, 46(2):236-280, 1999.
20. V. Gaede and O. Gunther. Multidimensional access methods. ACM Computing Surveys, 30(2):170-231, 1998.
21. A. Gionis, P. Indyk, and R. Motwani. Similarity search in high dimensions via hashing. In VLDB, pages 518-529, 1999.
22. J. Goldstein and R. Ramakrishnan. Contrast plots and p-sphere trees: Space vs. time in nearest neighbour searches. In VLDB, pages 429-440, 2000.
23. S. Har-Peled. A replacement for voronoi diagrams of near linear size. In FOCS, pages 94-103, 2001.
24. G. R. Hjaltason and H. Samet. Distance browsing in spatial databases. TODS, 24(2):265-318, 1999.
25. M. E. Houle and J. Sakuma. Fast approximate similarity search in extremely high-dimensional data sets. In ICDE, pages 619-630, 2005.
26. P. Indyk and R. Motwani. Approximate nearest neighbors: Towards removing the curse of dimensionality. In STOC, pages 604-613, 1998.
27. H. V. Jagadish, B. C. Ooi, K.-L. Tan, C. Yu, and R. Zhang. idistance: An adaptive b*-tree based indexing method for nearest neighbor search. TODS, 30(2):364-397, 2005.
28. F. Korn, B.-U. Pagel, and C. Faloutsos. On the 'dimensionality curse' and the 'self-similarity blessing'. TKDE, 13(1):96-111, 2001.
29. N. Koudas, B. C. Ooi, H. T. Shen, and A. K. H. Tung. Ldc: Enabling search by partial distance in a hyper-dimensional space. In ICDE, pages 6-17, 2004.
30. R. Krauthgamer and J. R. Lee. Navigating nets: simple algorithms for proximity search. In SODA, pages 798-807, 2004.
31. C. Li, E. Y. Chang, H. Garcia-Molina, and G. Wiederhold. Clustering for approximate similarity search in high-dimensional spaces. TKDE, 14(4):792-808, 2002.
32. K.-I. Lin, H. V. Jagadish, and C. Faloutsos. The tv-tree: An index structure for high-dimensional data. The VLDB Journal, 3(4):517-542, 1994.
33. Q. Lv, W. Josephson, Z. Wang, M. Charikar, and K. Li. Multi-probe lsh: Efficient indexing for high-dimensional similarity search. In VLDB, pages 950-961, 2007.
34. R. Panigrahy. Entropy based nearest neighbor search in high dimensions. In SODA, pages 1186-1195, 2006.
35. N. Roussopoulos, S. Kelley, and F. Vincent. Nearest neighbor queries. In SIGMOD, pages 71-79, 1995.
36. R. Weber, H.-J. Schek, and S. Blott. A quantitative analysis and performance study for similarity-search methods in high-dimensional spaces. In VLDB, pages 194-205, 1998.