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Communication Dans Un Congrès Année : 2000

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

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We 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.
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hal-00144234 , version 1 (02-05-2007)

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Jérôme Darmont, Christophe Fromantin, Stéphane Régnier, Le Gruenwald, Michel Schneider. Dynamic Clustering in Object-Oriented Databases: An Advocacy for Simplicity. ECOOP 2000 Symposium on Objects and Databases, Jun 2000, Sofia Antipolis, France. pp.71-85. ⟨hal-00144234⟩
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