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Conference Paper: DROLAP - A Dense-Region Based Approach to On-Line Analytical Processing

TitleDROLAP - A Dense-Region Based Approach to On-Line Analytical Processing
Authors
Issue Date1999
PublisherSpringer.
Citation
The 10th International Conference on Database and Expert Systems Applications (DEXA '99), Florence, Italy, 30 August - 3 September 1999. In Bench-Capon, TJ, Soda, G and Tjoa, AM (Eds.). Database and Expert Systems Applications, p. 761-770. Berlin; Heidelberg: Springer, 1999 How to Cite?
AbstractROLAP (Relational OLAP) and MOLAP (Multidimensional OLAP) are two opposing techniques for building On-line Analytical Processing (OLAP) systems. MOLAP has good query performance while ROLAP is based on mature RDBMS technologies. Many data warehouses contain sparse but clustered multidimensional data which neither ROLAP or MOLAP handles efficiently and scalably.We propose a denseregion-based OLAP (DROLAP) approach which surpasses both ROLAP and MOLAP in space efficiency and query performance. DROLAP takes the bests of ROLAP and MOLAP and combines them to support fast queries and high storage utilization. The core of building a DROLAP system lies in the mining of dense regions in a data cube, for which we have developed an efficient index-based algorithm EDEM to handle. Extensive performance studies consistently show that the DROLAP approach is superior to both MOLAP and ROLAP in handling sparse but clustered multidimensional data. Moreover, our EDEM algorithm is efficient and effective in identifying dense regions.
Persistent Identifierhttp://hdl.handle.net/10722/93261
ISBN
Series/Report no.Lecture Notes in Computer Science book series (LNCS, volume 1677)

 

DC FieldValueLanguage
dc.contributor.authorCheung, DWLen_HK
dc.contributor.authorZhou, Ben_HK
dc.contributor.authorKao, CMen_HK
dc.contributor.authorHu, Ken_HK
dc.contributor.authorLee, SDen_HK
dc.date.accessioned2010-09-25T14:55:45Z-
dc.date.available2010-09-25T14:55:45Z-
dc.date.issued1999en_HK
dc.identifier.citationThe 10th International Conference on Database and Expert Systems Applications (DEXA '99), Florence, Italy, 30 August - 3 September 1999. In Bench-Capon, TJ, Soda, G and Tjoa, AM (Eds.). Database and Expert Systems Applications, p. 761-770. Berlin; Heidelberg: Springer, 1999-
dc.identifier.isbn978-3-540-66448-2-
dc.identifier.urihttp://hdl.handle.net/10722/93261-
dc.description.abstractROLAP (Relational OLAP) and MOLAP (Multidimensional OLAP) are two opposing techniques for building On-line Analytical Processing (OLAP) systems. MOLAP has good query performance while ROLAP is based on mature RDBMS technologies. Many data warehouses contain sparse but clustered multidimensional data which neither ROLAP or MOLAP handles efficiently and scalably.We propose a denseregion-based OLAP (DROLAP) approach which surpasses both ROLAP and MOLAP in space efficiency and query performance. DROLAP takes the bests of ROLAP and MOLAP and combines them to support fast queries and high storage utilization. The core of building a DROLAP system lies in the mining of dense regions in a data cube, for which we have developed an efficient index-based algorithm EDEM to handle. Extensive performance studies consistently show that the DROLAP approach is superior to both MOLAP and ROLAP in handling sparse but clustered multidimensional data. Moreover, our EDEM algorithm is efficient and effective in identifying dense regions.-
dc.languageengen_HK
dc.publisherSpringer.-
dc.relation.ispartofDatabase and Expert Systems Applicationsen_HK
dc.relation.ispartofseriesLecture Notes in Computer Science book series (LNCS, volume 1677)-
dc.titleDROLAP - A Dense-Region Based Approach to On-Line Analytical Processingen_HK
dc.typeConference_Paperen_HK
dc.identifier.emailCheung, DWL: dcheung@cs.hku.hken_HK
dc.identifier.emailKao, CM: kao@cs.hku.hken_HK
dc.identifier.emailLee, SD: sdlee@cs.hku.hken_HK
dc.identifier.authorityCheung, DWL=rp00101en_HK
dc.identifier.authorityKao, CM=rp00123en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1007/3-540-48309-8_71-
dc.identifier.hkuros47956en_HK
dc.identifier.hkuros50348-

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