Dynamic orthogonal range queries in OLAP

Theoretical Computer Science

Volumn 296, Issue 3, 2003, Pages 487-510

  Poon, Chung Keung ^a  

^a CITY UNIVERSITY OF HONG KONG   (Hong Kong)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATIONAL COMPLEXITY; DATA STRUCTURES; PROBLEM SOLVING;

ON-LINE ANALYTICAL PROCESSING (OLAP);

QUERY LANGUAGES;

EID: 0037436772     PISSN: 03043975     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0304-3975(02)00741-7     Document Type: Conference Paper

References (37)

1. Agrawal R., Gupta A., Sarawagi S. Modeling multidimensional databases. 13th Internat. Conf. on Data Engineering, Birmingham, UK. 1997;232-243 IEEE, New York.
2. Z. Aviad, E. Shamir, A direct dynamic solution to range search and related problems for product regions, in: 22nd Ann. Symp. on Foundations of Computer Science, IEEE, Nashville, TN, October 1981, New York, pp. 123-126.
3. Bentley J.L. Multidimensional binary search trees used for associative searching. Comm. ACM. 18(9):1975;509-517.
4. Bentley J.L. Multidimensional divide-and-conquer. Comm. ACM. 23(4):1980;214-228.
5. Bentley J.L., Maurer H.A. Efficient worst-case data structures for range searching. Acta Inform. 13:1980;155-168.
6. Bentley J.L., Shamos M.I. A problem in multivariate statistics. algorithms, data structure and applications 15th Allerton Conf. on Communications, Control and Computing. 1977;193-201 University of Illinois Press, Champaign.
7. Bolour A. Optimal retrieval algorithms for small region queries. SIAM J. Comput. 10(4):1981;721-741.
8. Chan C.Y., Ioannidis Y.E. Hierarchical prefix cubes for range-sum queries. Proc. 25th Internat. Conf. on Very Large Data Bases, Edinburgh, UK. 1999;675-686 Morgan-Kaufmann, Los Altos, CA.
9. Chazelle B. Filtering search. a new approach to query-answering SIAM J. Comput. 15(3):1986;703-724.
10. Chazelle B. A functional approach to data structures and its use in multidimensional searching. SIAM J. Comput. 17(3):1988;427-462.
11. Chazelle B. Lower bounds for orthogonal range searching. I. The reporting case J. ACM. 37(2):1990;200-212.
12. Chazelle B. Lower bounds for orthogonal range searching. II. The arithmetic model J. ACM. 37(3):1990;439-463.
13. Chazelle B., Rosenberg B. Computing partial sums in multidimensional arrays. 5th Ann. Symp. on Computational Geometry, Saarbruchen, Germany. 1989;131-139 ACM, New York.
14. Cheung D.W., Zhou B., Kao B., Hu K., Lee S.D. DROLAP - a dense-region based approach to on-line analytical processing. 10th Internat. Conf. on Database and Expert Systems Applications (DEXA'99), Florence, Italy, Lecture Notes in Computer Science. Vol. 1677:1999;761-770 Springer, Berlin.
15. E.F. Codd, Providing OLAP (on-line analytical processing) to user-analysts: an IT mandate, Technical Report, E.F. Codd and Associates, 1993.
16. The OLAP Council, MD-API the OLAP application program interface version 0.5 specification, Technical Report, September 1996.
17. Finkel R.A., Bentley J.L. Quad trees. a data structure for retrievel on composite keys Acta Inform. 4:1974;1-9.
18. Fredman M. A lower bound on the complexity of orthogonal range queries. J. ACM. 28(4):1981;696-705.
19. Gabow H., Bentley J.L., Tarjan R.E. Scaling and related techniques for goemetry problems. Proc. 16th Ann. ACM Symp. on Theory of Computing, Washington, DC. 1984;135-143 ACM, New York.
20. Geffner S., Agrawal D., El Abbadi A., Smith T. Relative prefix sums. an efficient approach for querying dynamic OLAP data cubes 15th Internat. Conf. on Data Engineering, Sydney, Australia New York. 1999;328-335 IEEE, New York.
21. Gray J., Bosworth A., Layman A., Pirahesh H. Data cube. a relational aggregation operator generalizing group-by, cross-tab, and sub-totals 12th Internat. Conf. on Data Engineering, New Orleans, LA. 1996;152-159 IEEE, New York.
22. Harel D., Tarjan R.E. Fast algorithms for finding nearest common ancestors. SIAM J. Comput. 13(2):1984;338-355.
23. Ching-Tien Ho, R. Agrawal, N. Meggido, Jyh-Jong Tsay, Techniques for speeding up rang-max queries, Technical Report, IBM Research Report, April 1997.
24. Ching-Tien Ho, R. Agrawal, N. Megiddo, Ramakrishnan Srikant, Range queries in OLAP data cubes, in: ACM SIGMOD Conf. on the Management of Data, Tuscon, AZ, 1997, pp. 73-88.
25. Ho Ching-Tien, Bruck J., Agrawal R. Partial-sum queries in OLAP data cubes using covering codes. IEEE Trans. Comput. 47(12):1998;1326-1340.
26. Lee D.T., Wong C.K. Worst-case analysis for region and partial region searches in multi-dimensional binary search trees and balanced quad trees. Acta Inform. 9:1977;23-29.
27. Sin Yeung Lee, Tok Wang Ling, HuaGang Li Hierarchical compact cube for range-max queries. Proc. 26th Internat. Conf. on Very Large Data Bases, Cairo, Egypt. 2000;232-241 Morgan-Kaufmann, New York.
28. Chung Keung Poon, Orthogonal range queries in OLAP, in: 8th Internat. Conf. on Database Theory, Lecture Notes in Computer Science, Vol. 1973, London, Britain, Springer, Berlin, January 2001, pp. 361-374.
29. K.V. Ravi Kanth, A.K. Singh, Efficient dynamic range searching using data replication, Technical Report TRCS97-12, University of California at Santa Barbara, 1997.
30. M. Riedewald, D. Agrawal, Amr El Abbadi, Flexible data cubes for online aggregation, in: 8th Internat. Conf. on Database Theory, Lecture Notes in Computer Science, Vol. 1973, London, Britain, Springer, Berlin, January 2001, pp. 159-173.
31. Tarjan R.E. Efficiency of a good but not linear set union algorithm. J. ACM. 22:1975;215-225.
32. Vuillemin J. A unifying look at data structures. Comm. ACM. 23(4):1980;229-239.
33. Willard D.E. New data structures for orthogonal queries. SIAM J. Comput. 14(1):1985;232-253.
34. Willard D.E., Lueker G.S. Adding range restriction capability to dynamic data structures. J. ACM. 32(3):1985;597-617.
35. A. Yao, Space-time tradeoff for answering range queries, in: Proc. 14th Ann. ACM Symp. on Theory of Computing, San Francisco, CA, 1982, pp. 128-136.
36. Yao A. On the complexity of maintaining partial sums. SIAM J. Comput. 14(2):1985;277-288.
37. Zhou B., Cheung D.W., Kao B. A fast algorithm for density-based clustering in large database. Methodologies for Knowledge Discovery and Data Mining (PAKDD'99), Beijing, China, Lecture Notes in Artificial Intelligence. Vol. 1574:1999;338-349 Springer, Berlin.