Abstract
Range-aggregate queries on the data cube are powerful tools for analysis in data warehouse environments. Cubetree is a technique materializing a data cube through an R-tree. It provides efficient data accessibility, but involves some drawbacks to traverse all the internal and leaf nodes within given query ranges to compute range-aggregate queries. In this paper, we propose a novel index structure for materializing a data cube, called aggregate cubetree. Each record in all internal nodes of an aggregate cubetree stores the aggregate value of all child nodes of it. Therefore, range-aggregate queries on an aggregate cubetree can be processed without visiting child nodes whose parent node is fully included in the query range, by using the aggregate values in the records of each internal node. The aggregate cubetree is superior to the original cubetree because it can execute queries with a smaller number of node accesses, and shows even better performance than the original cubetree as the query range becomes larger.
This work was supported by the Brain Korea 21 Project
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References
S. Chaudhuri and U. Dayal, ”An overview of data warehousing and OLAP technology”, ACM SIGMOD Record 26(1), pages 65–74, 1997
R. Kimball, “The Data Warehouse Toolkit”, John Wiley & Sons, 1996
N. Roussopoulos, “Materialized Views and Data Warehouses”, SIGMOD Record, 27(1), pages 21–26, March 1998.
J. Gray, A. Bosworth, A. Layman, and H. Piramish, “Data Cube: A Relational Aggregation Operator Generalizing, Group-By, Crosstab, and Sub-Totals”, Int. Conference on Data Engineering, pages 152–159, 1996
A. Guttman. “R-Trees: A Dynamic Index Structure for Spatial Searching”, In Proceedings of ACM SIGMOD, pages 47–57, 1984
C. Ho, R. Agrawal, N. Megiddo, R. Srikant. “Range Queries in OLAP Data Cubes”, In Proceedings of ACM SIGMOD, pages 73–88, 1997
Nick Roussopoulos, Yannis Kotdis, Mena Roussopoulos, “Cubetree: Organization of and Bulk Incremental Update on the Data Cube”, In Proceedings of ACM SIGMOD, pages 89–99, 1997
Yannis Kotdis, Nick Roussopoulos, “An Alternative Storage Organization for ROLAP Aggregate Views Based on Cubetrees”, In Proceedings of ACM SIGMOD, pages 249–258, 1998
H. Gupta. “Selections of Views to Materialize in a Data Warehouse”, In Proceedings of ICDT, pages 98–112, Delphi, January 1997
I. S. Mumick, D. Quass, and B. S. Mumick. “Maintenance of Data Cubes and Summary Tables in a Warehouse”, In Proceedings of the ACM SIGMOD International Conference on Management of Data, pages 100–111, Tucson, Arizona, May 1997.
V. Harinarayan, A. Rajaraman, J. D. Ullman, “Implementing data cubes efficiently”, In Proceedings of the ACM SIGMOD Conference, pages 205–216, 1996
S. Sarawagi, R. Agrawal, A. Gupta, “On the computing the data cube”, Research Report, IBM Almaden Research Center, Sanjose, Ca, 1996
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 the ACM SIGMOD Conference on Management of Data, pages 322–331, Atlantic City, NJ, May 1990.
S. Agrawal, R. Agrawal, P. Deshpande, A. Gupta, J. Naughton, R. Ramakrishnan, and S. Sarawagi. “On the Computation of Multidimensional Aggregates”, In Proceedings of VLDB, pages 506–521, August 1996.
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© 2001 Springer-Verlag Berlin Heidelberg
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Hong, S., Song, B., Lee, S. (2001). Efficient Execution of Range-Aggregate Queries in Data Warehouse Environments. In: S.Kunii, H., Jajodia, S., Sølvberg, A. (eds) Conceptual Modeling — ER 2001. ER 2001. Lecture Notes in Computer Science, vol 2224. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45581-7_23
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DOI: https://doi.org/10.1007/3-540-45581-7_23
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