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Article: Towards the building of a dense-region-based OLAP system

TitleTowards the building of a dense-region-based OLAP system
Authors
KeywordsData cube
Data warehouse
Dense region
Multidimensional data base
OLAP
Issue Date2001
PublisherElsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/datak
Citation
Data And Knowledge Engineering, 2001, v. 36 n. 1, p. 1-27 How to Cite?
AbstractOn-line analytical processing (OLAP) has become a very useful tool in decision support systems built on data warehouses. Relational OLAP (ROLAP) and multidimensional OLAP (MOLAP) are two popular approaches for building OLAP systems. These two approaches have very different performance characteristics: MOLAP has good query performance but bad space efficiency, while ROLAP can be built on mature RDBMS technology but it needs sizable indices to support it. Many data warehouses contain many small clustered multidimensional data (dense regions), with sparse points scattered around in the rest of the space. For these databases, we propose that the dense regions be located and separated from the sparse points. The dense regions can subsequently be represented by small MOLAPs, while the sparse points are put in a ROLAP table. Thus the MOLAP and ROLAP approaches can be integrated in one structure to build a high performance and space efficient dense-region-based data cube. In this paper, we define the dense region location problem as an optimization problem and develop a chunk scanning algorithm to compute dense regions. We prove a lower bound on the accuracy of the dense regions computed. Also, we analyze the sensitivity of the accuracy on user inputs. Finally, extensive experiments are performed to study the efficiency and accuracy of the proposed algorithm. © 2001 Elsevier Science B.V. All rights reserved.
Persistent Identifierhttp://hdl.handle.net/10722/88974
ISSN
2015 Impact Factor: 1.5
2015 SCImago Journal Rankings: 1.258
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorCheung, DWen_HK
dc.contributor.authorZhou, Ben_HK
dc.contributor.authorKao, Ben_HK
dc.contributor.authorKan, Hen_HK
dc.contributor.authorLee, SDen_HK
dc.date.accessioned2010-09-06T09:50:48Z-
dc.date.available2010-09-06T09:50:48Z-
dc.date.issued2001en_HK
dc.identifier.citationData And Knowledge Engineering, 2001, v. 36 n. 1, p. 1-27en_HK
dc.identifier.issn0169-023Xen_HK
dc.identifier.urihttp://hdl.handle.net/10722/88974-
dc.description.abstractOn-line analytical processing (OLAP) has become a very useful tool in decision support systems built on data warehouses. Relational OLAP (ROLAP) and multidimensional OLAP (MOLAP) are two popular approaches for building OLAP systems. These two approaches have very different performance characteristics: MOLAP has good query performance but bad space efficiency, while ROLAP can be built on mature RDBMS technology but it needs sizable indices to support it. Many data warehouses contain many small clustered multidimensional data (dense regions), with sparse points scattered around in the rest of the space. For these databases, we propose that the dense regions be located and separated from the sparse points. The dense regions can subsequently be represented by small MOLAPs, while the sparse points are put in a ROLAP table. Thus the MOLAP and ROLAP approaches can be integrated in one structure to build a high performance and space efficient dense-region-based data cube. In this paper, we define the dense region location problem as an optimization problem and develop a chunk scanning algorithm to compute dense regions. We prove a lower bound on the accuracy of the dense regions computed. Also, we analyze the sensitivity of the accuracy on user inputs. Finally, extensive experiments are performed to study the efficiency and accuracy of the proposed algorithm. © 2001 Elsevier Science B.V. All rights reserved.en_HK
dc.languageengen_HK
dc.publisherElsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/dataken_HK
dc.relation.ispartofData and Knowledge Engineeringen_HK
dc.rightsData & Knowledge Engineering. Copyright © Elsevier BV.en_HK
dc.subjectData cubeen_HK
dc.subjectData warehouseen_HK
dc.subjectDense regionen_HK
dc.subjectMultidimensional data baseen_HK
dc.subjectOLAPen_HK
dc.titleTowards the building of a dense-region-based OLAP systemen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0169-023X&volume=36&issue=1&spage=1&epage=27&date=2001&atitle=Towards+the+Building+of+a+Dense-Region-Based+OLAP+Systemen_HK
dc.identifier.emailCheung, DW:dcheung@cs.hku.hken_HK
dc.identifier.emailKao, B:kao@cs.hku.hken_HK
dc.identifier.authorityCheung, DW=rp00101en_HK
dc.identifier.authorityKao, B=rp00123en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1016/S0169-023X(00)00027-6en_HK
dc.identifier.scopuseid_2-s2.0-0345776954en_HK
dc.identifier.hkuros57267en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-0345776954&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume36en_HK
dc.identifier.issue1en_HK
dc.identifier.spage1en_HK
dc.identifier.epage27en_HK
dc.identifier.isiWOS:000166263300001-
dc.publisher.placeNetherlandsen_HK
dc.identifier.scopusauthoridCheung, DW=34567902600en_HK
dc.identifier.scopusauthoridZhou, B=7401906648en_HK
dc.identifier.scopusauthoridKao, B=35221592600en_HK
dc.identifier.scopusauthoridKan, H=7101603406en_HK
dc.identifier.scopusauthoridLee, SD=37056848600en_HK

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