Implementation of multidimensional index structures for knowledge discovery in relational databases

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 1676, Issue , 1999, Pages 261-270

  Berchtold, Stefan ^a   Böhm, Christian ^a,b   Kriegel, Hans Peter ^b   Michel, Urs ^b  

^a STB Software Technologie Beratung   (Germany)

^b UNIVERSITY OF MUNICH   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; DATA MINING; DATA WAREHOUSES; QUERY PROCESSING; RELATIONAL DATABASE SYSTEMS; WAREHOUSES;

DATA MINING ALGORITHM; HIGH DIMENSIONAL DATA; INDEX STRUCTURE; MULTI-DIMENSIONAL INDEX STRUCTURES; QUERY PROCESSOR; RELATIONAL DATABASE; RULE MINING ALGORITHMS; TIME-CONSUMING TASKS;

CLUSTERING ALGORITHMS;

EID: 84958052483     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-48298-9_28     Document Type: Conference Paper

References (28)

1. Agrawal R., Lin K., Sawhney H., Shim K.: ‘Fast Similarity Search in the Presence of Noise, Scaling, and Translation in Time-Series Databases’, Proc. of the 21st Conf. on Very Large Databases, 1995, pp. 490-501.
2. Agrawal R., Srikant R.: ’Fast Algorithms for Mining Association Rules’, Proc. of the 20st Conf. on Very Large Databases, Chile, 1995, pp. 487-499.
3. Berchtold S., Böhm C., Braunmueller B., Keim D. A., Kriegel H.-P.: ‘Fast Similarity Search in Multimedia Databases’, Proc. ACM SIGMOD Int. Conf. on Management of Data, 1997, Tucson, Arizona.
4. Berchtold S., Böhm C., Kriegel H.-P.: ‘The Pyramid-Technique: Towards indexing beyond the Curse of Dimensionality’, Proc. ACM SIGMOD Int. Conf. on Management of Data, Seattle, pp. 142-153,1998.
5. Berchtold S., Böhm C., Kriegel H.-P.: ‘Improving the Query Performance of High- Dimensional Index Structures Using Bulk-Load Operations’, 6th. Int. Conf. on Extending Database Technology, Valencia, 1998.
6. Berchtold S., Böhm C., Keim D., Kriegel H.-P.: ‘A Cost Model For Nearest Neighbor Search in High-Dimensional Data Space’, ACM PODS Symposium on Principles of Database Systems, 1997, Tucson, Arizona.
7. Bentley J.L.: ‘Multidimensional Search Trees Used for Associative Searching‘, Communications of the ACM, Vol. 18, No. 9, pp. 509-517, 1975.
8. Bentley J. L.: ‘Multidimensiuonal Binary Search in Database Applications’, IEEE Trans. Software Eng. 4(5), 1979, pp. 397-409.
9. Berchtold S., Keim D., Kriegel H.-P.: ‘The X-tree: An Index Structure for High- Dimensional Data’, 22nd Conf. on Very Large Databases, 1996.
10. Beckmann N., Kriegel H.-P., Schneider R., Seeger B.: ‘The R*-tree: An Efficient and Robust Access Method for Points and Rectangles’, Proc. ACM SIGMOD Int. Conf. on Management of Data, Atlantic City, NJ, 1990.
11. Böhm C.: ‘Efficiently Indexing High-Dimensional Data Spaces’, Ph.D. Thesis, Faculty for Mathematics and Computer Science, University of Munich, 1998.
12. Ester M., Kriegel H.-P., Sander J., Xu X.: ’Incremental Clustering for Mining in a Data Warehousing Environment’, Proc. 24th Int. Conf. on Very Large Databases (VLDB’98), NY, 1998, pp. 323-333.
13. Faloutsos C.: ‘Multiattribute Hashing Using Gray Codes’, Proc. ACM SIGMOD Int. Conf. on Management of Data, 1985, pp. 227-238.
14. Finkel R, Bentley J.L. ‘Quad Trees: A Data Structure for Retrieval of Composite Keys’, Acta Informatica 4(1), 1974, pp. 1-9.
15. Faloutsos C., Roseman S.: ‘Fractals for Secondary Key Retrieval’, Proc. 8th ACM SIGACT/SIGMOD Symp. on Principles of Database Systems, 1989, pp. 247-252.
16. Guttman A.: ‘R-trees: A Dynamic Index Structure for Spatial Searching’, Proc. ACM SIGMOD Int. Conf. on Management of Data, 1984.
17. Ho C.T., Agrawal R., Megiddo N., Srikant R.: Range Queries in OLAP Data Cubes. SIGMOD Conference 1997: 73-88
18. Hjaltason G. R., Samet H.: ‘Ranking in Spatial Databases’, Proc. 4th Int. Symp. on Large Spatial Databases, Portland, ME, 1995, pp. 83-95.
19. Jagadish H. V.: ‘Linear Clustering of Objects with Multiple Attributes’, Proc. ACM SIGMOD Int. Conf. on Management of Data, Atlantic City, NJ, 1990, pp. 332-342.
20. Jain R, White D.A.: ‘Similarity Indexing: Algorithms and Performance’, Proc. SPIE Storage and Retrieval for Image and Video Databases IV, Vol. 2670, San Jose, CA, 1996, pp. 62-75.
21. Katayama N., Satoh S.: ‘The SR-tree: An Index Structure for High-Dimensional Nearest Neighbor Queries’, Proc. ACM SIGMOD Int. Conf. on Management of Data, 1997, pp. 369-380.
22. Lin K., Jagadish H. V., Faloutsos C.: ‘The TV-Tree: An Index Structure for High- Dimensional Data’, VLDB Journal, Vol. 3, pp. 517-542, 1995.
23. Lomet D., Salzberg B.: ‘The hB-tree: A Robust Multiattribute Search Structure’, Proc. 5th IEEE Int. Conf. on Data Eng., 1989, pp. 296-304.
24. Mehrotra R., Gary J.: ‘Feature-Based Retrieval of Similar Shapes’, Proc. 9th Int. Conf. on Data Engeneering, 1993.
25. Nievergelt J., Hinterberger H., Sevcik K. C.: ‘The Grid File: An Adaptable, Symmetric Multikey File Structure’, ACM Trans. on Database Systems, Vol. 9, No. 1, 1984, pp. 38-71.
26. White D.A., Jain R.: ‘Similarity indexing with the SS-tree’, Proc. 12th Int. Conf on Data Engineering, New Orleans, LA, 1996.
27. Weber R., Scheck H.-J., Blott S.: ‘A Quantitative Analysis and Performance Study for Similarity-Search Methods in High-Dimensional Spaces’, Proc. Int. Conf. on Very Large Databases, New York, 1998.
28. Wallace T., Wintz P.: ‘An Efficient Three-Dimensional Aircraft Recognition Algorithm Using Normalized Fourier Descriptors’, Computer Graphics and Image Processing, Vol. 13, pp. 99-126, 1980.