Lixin Fu
- 1.S. Agarwal, R. Agrawal, P. Deshpande, J. Naughton, S. Sarawagi, and R. Ramakrishnan, "On The Computation of Multidimensional Aggregates," in Proceedings of the International Conference on Very Large Databases, Mumbai (Bombai), India, 1996.]] Google Scholar
Digital Library
- 2.R. Agrawal, A. Gupta, and S. Sarawagi, "Modeling Multidimensional Databases," in Proceedings of the Thirteenth International Conference on Database Engineering, Birmingham, U.K., 1997.]] Google ScholarDigital Library
- 3.Arbor Systems, "Large-Scale Data Warehousing Using Hyperion Essbase OLAP Technology," Arbor Systems, White Paper, www.hyperion.com/whitepapers.cfm.]]Google Scholar
- 4.S. Berchtold and D. A. Keim, "High-dimensional index structures database support for next decade's applications (tutorial)," in Proc. of the ACM SIGMOD Intl. Conference on Management of Data, Seattle, WA, pp. 501, 1998.]] Google ScholarDigital Library
- 5.C. Y. Chan and Y. E. Ioannidis, "Bitmap Index Design and Evaluation," in Proceedings of the ACM SIGMOD International Conference on Management of Data, Seattle, WA, pp. 355-366, 1998.]] Google ScholarDigital Library
- 6.S. Chaudhuri and U. Dayal, "Data Warehousing and OLAP for Decision Support," SIGMOD Record (ACM Special Interest Group on Management of Data), 26:2, pp. 507-508, 1997.]] Google ScholarDigital Library
- 7.S. Chaudhuri and U. Dayal, "An Overview of Data Warehousing and OLAP Technology," SIGMOD Record, 26:1, pp. 65-74, 1997.]] Google ScholarDigital Library
- 8.E. F. Codd, S. B. Codd, and C. T. Salley, "Providing OLAP (online analytical processing) to user-analysts: An IT mandate," Technical Report, www.arborsoft.com/OLAP.html.]]Google Scholar
- 9.D. Comer, "The Ubiquitous Btree," ACM Computing Surveys, 11:2, pp. 121-137, 1979.]] Google ScholarDigital Library
- 10.S. Goil and A. Choudhary, "High Performance OLAP and Data Mining on Parallel Computers,," Journal of Data Mining and Knowledge Discovery, 1:4, pp. 391-417, 1997.]] Google ScholarDigital Library
- 11.S. Goil and A. Choudhary, "PARSIMONY: An Infrastructure for Parallel Multidimensional Analysis and Data Mining,," Journal of Parallel and Distributed Computing, to appear.]] Google ScholarDigital Library
- 12.J. Gray, S. Chaudhuri, A. Bosworth, A. Layman, D. Reichart, M. Venkatrao, F. Pellow, and H. Pirahesh, "Data Cube: A Relational Aggregation Operator Generalizing Group-By, Cross-Tab, and Sub-Totals," Data Mining and Knowledge Discovery, 1:1, pp. 29-53, 1997.]] Google ScholarDigital Library
- 13.A. Gupta, V. Harinarayan, and D. Quass, "Aggregate-query Processing in Data Warehousing Environments," in Proceedings of the Eighth International Conference on Very Large Databases, Zurich, Switzerland, pp. 358-369, 1995.]] Google ScholarDigital Library
- 14.H. Gupta and I. Mumick, "Selection of Views to Materialize Under a Maintenance Cost Constraint," in Proceedings of the International Conference on Management of Data, Jerusalem, Israel, pp. 453-470, 1999.]] Google ScholarDigital Library
- 15.J. Han, "Towards On-Line Analytical Mining in Large Databases," SIGMOD Record, 27:1, pp. 97-107, 1998.]] Google ScholarDigital Library
- 16.V. Harinarayan, A. Rajaraman, and J. D. Ullman, "Implementing data cubes efficiently," SIGMOD Record (ACM Special Interest Group on Management of Data), 25:2, pp. 205-216, 1996.]] Google ScholarDigital Library
- 17.Information Advantage, "Business Intelligence," White Paper, 1998, www.sterling.com/eureka/.]]Google Scholar
- 18.Informix Inc., "Informix MetaCube 4.2, Delivering the Most Flexible Business-Critical Decision Support Environments," Informix, Menlo Park, CA, White Paper, www.informix.com/informix/products/tools/me tacube/datasheet.htm.]]Google Scholar
- 19.W. Labio, D. Quass, and B. Adelberg, "Physical Database Design for Data Warehouses," in Proceedings of the International Conference on Database Engineering, Birmingham, England, pp. 277-288, 1997.]] Google ScholarDigital Library
- 20.M. Lee and J. Hammer, "Speeding Up Warehouse Physical Design Using A Randomized Algorithm," in Proceedings of the International Workshop on Design and Management of data Warehouses (DMDW '99), Heidelberg, Germany, 1999,]]Google Scholar
- 21.D. Lomet, Bulletin of the Technical Committee on Data Engineering, 18, IEEEE Computer Society, 1995.]]Google Scholar
- 22.Z. Michalewicz, Statistical and Scientific Databases, Ellis Horwood, 1992.]] Google ScholarDigital Library
- 23.Microsoft Corp., "Microsoft SQL Server," Microsoft, Seattle, WA, White Paper, www.microsoft.com/federal/sql7/white.htm.]]Google Scholar
- 24.MicroStrategy Inc., "The Case For Relational OLAP," MicroStrategy, White Paper, www.microstrategy.com/publications/whitepaper s/Case4Rolap/execsumm.htm.]]Google Scholar
- 25.P. O'Neil and D. Quass, "Improved Query Performance with Variant Indexes," SIGMOD Record (ACM Special Interest Group on Management of Data), 26:2, pp. 38-49, 1997.]] Google ScholarDigital Library
- 26.P. E. O'Neil, "Model 204 Architecture and Performance," in Proc. of the 2nd International Workshop on High Performance Transaction Systems, Asilomar, CA, pp. 40-59, 1987.]] Google ScholarDigital Library
- 27.Oracle Corp., "Oracle Express OLAP Technology," www.oracle.com/olap/index.html.]]Google Scholar
- 28.Pilot Software Inc., "An Introduction to OLAP Multidimensional Terminology and Technology," Pilot Software, Cambridge, MA, White Paper, www.pilotsw.com/olap/olap.htm.]]Google Scholar
- 29.Redbrick Systems, "Aggregate Computation and Management," Redbrick, Los Gatos, CA, White Paper, www.informix.com/informix/solutions/dw/redb rick/wpapers/redbrickvistawhitepaper.html.]]Google Scholar
- 30.Redbrick Systems, "Decision-Makers, Business Data and RISQL," Informix, Los Gatos, CA, White Paper, 1997.]]Google Scholar
- 31.J. Srivastava, J. S. E. Tan, and V. Y. Lum, "TBSAM: An Access Method for Efficient Processing of Statistical Queries," IEEE Transactions on Knowledge and Data Engineering, 1:4, pp. 414- 423, 1989.]] Google ScholarDigital Library
- 32.W. P. Yan and P. Larson, "Eager Aggregation and Lazy Aggregation," in Proceedings of the Eighth International Conference on Very Large Databases, Zurich, Switzerland, pp. 345-357, 1995.]] Google ScholarDigital Library
- 33.Y. Zhao, P. M. Deshpande, and J. F. Naughton, "An Array- Based Algorithm for Simultaneous Multidimensional Aggregates," SIGMOD Record (ACM Special Interest Group on Management of Data), 26:2, pp. 159-170, 1997.]] Google ScholarDigital Library
Index Terms
- CubiST: a new algorithm for improving the performance of ad-hoc OLAP queries
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