Partial-sum queries in OLAP data cubes using covering codes

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Partial-sum queries in OLAP data cubes using covering codes

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Symposium on Principles of Database Systems archive
Proceedings of the sixteenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems table of contents

Tucson, Arizona, United States

Pages: 228 - 237

Year of Publication: 1997

ISBN:0-89791-910-6

Authors

Ching-Tien Ho	IBM Almaden Research Center, 650 Harry Road, San Jose, CA
Jehoshua Bruck	California Institute of Technology, Mail Stop 136-93, Pasadena, CA
Rakesh Agrawal	IBM Almaden Research Center, 650 Harry Road, San Jose, CA

Sponsor

SIGMOD: ACM Special Interest Group on Management of Data

Publisher

ACM New York, NY, USA

Bibliometrics

Downloads (6 Weeks): 1, Downloads (12 Months): 15, Citation Count: 6

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REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

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CITED BY 6

	Ching-Tien Ho , Rakesh Agrawal , Nimrod Megiddo , Ramakrishnan Srikant, Range queries in OLAP data cubes, ACM SIGMOD Record, v.26 n.2, p.73-88, June 1997
	Mirek Riedewald , Divyakant Agrawal , Amr El Abbadi, Managing and analyzing massive data sets with data cubes, Handbook of massive data sets, Kluwer Academic Publishers, Norwell, MA, 2002
	Woo Suk Yang , Yon Dohn Chung , Myoung Ho Kim, The RD-Tree: a structure for processing partial-MAX/MIN Queries in OLAP, Information Sciences—Applications: An International Journal, v.146 n.1-4, p.137-149, October 2002
	Yon Dohn Chung , Woo Suk Yang , Myoung Ho Kim, An efficient, robust method for processing of partial top-k/bottom-k queries using the RD-Tree in OLAP, Decision Support Systems, v.43 n.2, p.313-321, March, 2007
	Laks V. S. Lakshmanan , Jian Pei , Jiawei Han, Quotient cube: how to summarize the semantics of a data cube, Proceedings of the 28th international conference on Very Large Data Bases, p.778-789, August 20-23, 2002, Hong Kong, China
	Lixin Fu , Sanguthevar Rajasekaran, Evaluating holistic aggregators efficiently for very large datasets, The VLDB Journal — The International Journal on Very Large Data Bases, v.13 n.2, p.148-161, May 2004