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Conference Paper: Cross table cubing: mining iceberg cubes from data warehouses

TitleCross table cubing: mining iceberg cubes from data warehouses
Authors
Issue Date2005
PublisherSociety for Industrial and Applied Mathematics.
Citation
The 5th SIAM International Conference on Data Mining, Newport Beach, CA., 21-23 April 2005. In Conference Proceedings, 2005, p. 461-465 How to Cite?
AbstractAll of the existing (iceberg) cube computation algorithms assume that the data is stored in a single base table, however, in practice, a data warehouse is often organized in a schema of multiple tables, such as star schema and snowflake schema. In terms of both computation time and space, materializing a universal base table by joining multiple tables is often very expensive or even unaffordable in real data warehouses. In this paper, we investigate the problem of computing iceberg cubes from data warehouses. Surprisingly, our study shows that computing iceberg cube from multiple tables directly can be even more efficient in both space and runtime than computing from a materialized universal base table. We develop an efficient algorithm, CTC (for Cross Table Cubing) to tackle the problem. An extensive performance study on synthetic data sets demonstrates that our new approach is efficient and scalable for large data warehouses.
Persistent Identifierhttp://hdl.handle.net/10722/45530
ISBN

 

DC FieldValueLanguage
dc.contributor.authorCho, Men_HK
dc.contributor.authorPei, Jen_HK
dc.contributor.authorCheung, DWLen_HK
dc.date.accessioned2007-10-30T06:28:33Z-
dc.date.available2007-10-30T06:28:33Z-
dc.date.issued2005en_HK
dc.identifier.citationThe 5th SIAM International Conference on Data Mining, Newport Beach, CA., 21-23 April 2005. In Conference Proceedings, 2005, p. 461-465en_HK
dc.identifier.isbn0-89871-593-8en_HK
dc.identifier.urihttp://hdl.handle.net/10722/45530-
dc.description.abstractAll of the existing (iceberg) cube computation algorithms assume that the data is stored in a single base table, however, in practice, a data warehouse is often organized in a schema of multiple tables, such as star schema and snowflake schema. In terms of both computation time and space, materializing a universal base table by joining multiple tables is often very expensive or even unaffordable in real data warehouses. In this paper, we investigate the problem of computing iceberg cubes from data warehouses. Surprisingly, our study shows that computing iceberg cube from multiple tables directly can be even more efficient in both space and runtime than computing from a materialized universal base table. We develop an efficient algorithm, CTC (for Cross Table Cubing) to tackle the problem. An extensive performance study on synthetic data sets demonstrates that our new approach is efficient and scalable for large data warehouses.en_HK
dc.languageengen_HK
dc.publisherSociety for Industrial and Applied Mathematics.en_HK
dc.relation.ispartofSIAM International Conference on Data Mining-
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.titleCross table cubing: mining iceberg cubes from data warehousesen_HK
dc.typeConference_Paperen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0-89871-593-8&volume=&spage=461&epage=465&date=2005&atitle=Cross+table+cubing:+mining+iceberg+cubes+from+data+warehousesen_HK
dc.description.naturepublished_or_final_versionen_HK
dc.identifier.hkuros103219-

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