Dynamic Clustering in Object-Oriented Databases: An Advocacy for Simplicity

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dc.creatorDarmont, Jérôme
dc.creatorFromantin, Christophe
dc.creatorRégnier, Stéphane
dc.creatorGruenwald, Le
dc.creatorSchneider, Michel
dc.date2007-05-02
dc.date.accessioned2026-07-07T07:59:08Z
dc.date.available2026-07-07T07:59:08Z
dc.descriptionWe present in this paper three dynamic clustering techniques for Object-Oriented Databases (OODBs). The first two, Dynamic, Statistical & Tunable Clustering (DSTC) and StatClust, exploit both comprehensive usage statistics and the inter-object reference graph. They are quite elaborate. However, they are also complex to implement and induce a high overhead. The third clustering technique, called Detection & Reclustering of Objects (DRO), is based on the same principles, but is much simpler to implement. These three clustering algorithm have been implemented in the Texas persistent object store and compared in terms of clustering efficiency (i.e., overall performance increase) and overhead using the Object Clustering Benchmark (OCB). The results obtained showed that DRO induced a lighter overhead while still achieving better overall performance.
dc.identifierhttps://arxiv.org/abs/0705.0281
dc.identifierhttp://arxiv.org/abs/0705.0281
dc.identifierLNCS, Vol. 1944 (06/2000) 71-85
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/128258
dc.subjectDatabases
dc.titleDynamic Clustering in Object-Oriented Databases: An Advocacy for Simplicity
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