Improvements in distance-based indexing

Proceedings of the International Conference on Scientific and Statistical Database Management, SSDBM

Volumn 16, Issue , 2004, Pages 161-170

  Taşan, Murat ^a   Meral Ozsoyoglu Z ^a  

^a Case Western Reserve University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DATA SETS; DISTANCE-BASED INDEXING; METRIC SPACES; SPATIAL INDEXING;

ALGORITHMS; COMPUTATIONAL METHODS; DATABASE SYSTEMS; HEURISTIC METHODS; INFORMATION RETRIEVAL; PROBABILITY; PROBLEM SOLVING; SET THEORY; TREES (MATHEMATICS); VECTORS;

INDEXING (OF INFORMATION);

EID: 5444273023     PISSN: 10993371     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (45)

1. R. Baeza-Yates. Searching: an algorithmic tour. In Encyclopedia of Computer Science and Technology, volume 37, pages 331-359. Marcel Dekker Inc., 1997.
2. R. Baeza-Yates, W. Cunto, U. Manber, and S. Wu. Proximity matching using fixed-queries trees. In Proc. CPM'94, pages 198-212, 1994.
3. R. Bayer and E. M. McCreight. Organization and maintenance of large ordered indexes. Acta Information, 1(3):173-189, 1972.
4. N. Beckmann, H.-P. Kriegel, R. Schneider, and B. Seeger. The r*-tree: An efficient and robust access method for points and rectangles,. In Proceedings of ACM SIGMOD Int'l. Conf. on Management of Data, pages 322-331, 1990.
5. J. L. Bentley. Multidimensional binary search trees used for associative searching. Communications of the ACM, 18(9):509-517, September 1975.
6. J. L. Bentley. Multidimensional binary search in database applications. IEEE Trans. Software Eng., 4(5):333-340, 1979.
7. J. L. Bentley and J. H. Friedman. Data structures for range searching. ACM Computing Surveys, 11(4):397-409, December 1979.
8. S. Berchtold, C. Böhm, D. A. Keim, and H.-P. Kriegel. A cost model for nearest neighbor search in high dimensional data spaces. In Proc. 16th ACM SIGACT-SIGMOD-SIGART Symp. on Principles of Database Systems (PODS), pages 1-12, Tuscon, AZ, 1997. Best Paper Award.
9. S. Berchtold, D. A. Keim, and H.-P. Kriegel. The X-tree: An index structure for high-dimensional data. In T. M. Vijayaraman, A. P. Buchmann, C. Mohan, and N. L. Sarda, editors, Proceedings of the 22nd International Conference on Very Large Databases, pages 28-39, San Francisco, U.S.A., 1996. Morgan Kaufmann Publishers.
10. K. Beyer, J. Goldstein, R. Ramakrishnan, and U. Shaft. When is "nearest neighbor" meaningful? Lecture Notes in Computer Science, 1540:217-235, 1999.
11. T. Bozkaya and M. Ozsoyoglu. Distance-based indexing for high-dimensional metric spaces, pages 357-368, 1997.
12. S. Brin. Near neighbor search in large metric spaces. In The VLDB Journal, pages 574-584, 1995.
13. W. Burkhard and R. Keller. Some approaches to best-match file search. Communications of the ACM, 16(4):230-236, 1973.
14. I. Calantari and G. McDonald. A data structure and an algorithm for the nearest point problem. IEEE Transaction on Software Eng., 9(5), 1983.
15. E. Chávez, J. L. Marroquín, and G. Navarro. Overcoming the curse of dimensionality.
16. E. Chávez, E. Navarro, G. Baeza-Yates, and R. Marroqu. Searching in metric spaces, 1999.
17. A. W. chee Fu, P. M. shuen Chan, Y.-L. Cheung, and Y. S. Moon. Dynamic vp-tree indexing for n-nearest neighbor search given pair-wise distances. The VLDB Journal, 9(2):154-173, July 2000.
18. T. C. Chiuech. Content-based image indexing. In. Proceedings of the 20th VLDB Conference, pages 582-593.
19. P. Ciaccia, M. Patella, and P. Zezula. M-tree: An efficient access method for similarity search in metric spaces. In The VLDB Journal, pages 426-435, 1997.
20. D. Comer. The ubiquitous b-tree. ACM Computing Surveys, 11(2):121-128, June 1979.
21. C. Faloutsos and K.-I. Lin. FastMap: A fast algorithm for indexing, data-mining and visualization of traditional and multimedia datasets. In. M. J. Carey and D. A. Schneider, editors, Proceedings of the 1995 ACM SIGMOD International Conference on Management of Data, pages 163-174, San Jose, California, 22-25 1995.
22. V. Gaede and O. Günther. Multidimensional access methods. ACM Computing Surveys, 30(2):170-231, 1998.
23. A. Guttman. R-trees: A dynamic index structure for spatial searching. In C.-Y. C. et al., editor, In Proceedings of SIGMOD, pages 47-57, Boston, Massachusetts, 1984.
24. J. M. Hellerstein, J. F. Naughton, and A. Pfeffer. Generalized search trees for database systems. In U. Dayal, P. M. D. Gray, and S. Nishio, editors, Proc. 21st Int. Conf. Very Large Data Bases, VLDB, pages 562-573. Morgan Kaufmann, 11-15 1995.
25. A. Hinneburg, C. C. Aggarwal, and D. A. Keim. What is the nearest neighbor in high dimensional spaces? In The VLDB Journal, pages 506-515, 2000.
26. G. R. Ejaltason and E. Samet. Ranking in spatial databases. In Symposium on Large Spatial Databases, pages 83-95, 1995.
27. D. Huttenlocher, D. Klanderman, and A. Rucklige. Comparing images using the Hausdorff distance. IEEE Transactions on Pattern Analysis and Machine Intelligence, 15(9):850-863, September 1993.
28. T. Kahveci and A. Singh. An efficient index structure for string databases. In VLDB, pages 351-360, Roma, Italy, September 2001.
29. I. Kamel and C. Faloutsos. Hilbert R-tree: An Improved R-tree using Fractals. In Proceedings of the Twentieth International Conference an Very Large Databases, pages 500-509, Santiago, Chile, 1994
30. L. Kaufman and P. J. Rousseeuw. Finding Groups in Data: An Introduction to Cluster Analysis. John Wiley & Sons, 1990.
31. J. B. Kruskal and M. Wish. Multidimensional Scaling. Sage Piblications, Beverly Hills, CA, 1978.
32. V. A. Levenshtein. Binary codes capable of correcting deletions, insertions, and reversals. Cybernetics and Control Theory, 10(8):707-710, 1966.
33. J. McQueen. Some methods for classification and analysis of multivariate observations. In In Proceedings of 5th Berkeley Symposium on Mathematics, Statistics and Probability, pages 281-298, 1967.
34. J. Mic, L. author Oncina and E. Vidal. A new version of the nearest neighbour approximating anti eliminating search algorithm (aesa) with linear preprocessing-time and memory requirements. Pattern Recognition Letters, 1994.
35. H. Nolteimer, K. Verbarg, and C. Zirkslbach. Monotonous bisector trees - a tool for efficient partitioning of complex schenes of geometric objects. In. Data Structures and Efficient Algorithms, pages 186-203. Springer-Verlag, 1992. LNCS 594.
36. J. A. Orenstein and T. H. Merrett. A class of data structures for associative searching. In In Proceedings of the Third ACM SIGACT-SIGMOD Symposium on Principles of Database Systems, pages 181-190, Waterloo, Ontario, Canada, April 1984. ACM.
37. N. Roussopoulos and D. Leifker. Direct spatial search on pictorial databases using packed r-trees. In Proceedings of the ACM SIGMOD, May 1985.
38. S. C. Sahinalp, M. Tasan, J. Macker, and Z. M. Ozsoyoglu. Distance based indexing for sequence proximity search. In IEEE Conference on Data Engineering, Bangalore, India, 2003.
39. M. Tamminen. Comment on quad- and octtrees. Communications of the ACM, 27(3):248-249, March 1984.
40. W. S. Torgerson. Multidimensional scaling: Theory and method. Psychometrika, 17:401-419, 1952.
41. J. Uhlmann. Satisfying general proximity/similarity queries with metric trees. Information Processing Letters, (40):175-179, 1991.
42. E. Vidal. An algorithm for finding nearest neighbors in (approximately) constant average time complexity. Pattern Recognition Letters, 4:145-157, July 1986.
43. Yianilos. Data structures and algorithms for nearest neighbor search in general metric spaces. In SODA: ACM-SIAM Symposium on Discrete Algorithms (A Conference on Theoretical and Experimental Analysis of Discrete Algorithms), 1993.
44. P. Yianilos. Excluded middle vantage point forests for nearest neighbor search, 1999.
45. F. W. Young. Multidimensional Scaling: History, Thoery, and Applications. Lawrence Erlbaum Associates, Hillsdale, New Jersey, 1987.