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

Jerome Darmont, Christophe Fromantin, Stephane Régnier, Le Gruenwald, Michel Schneider
2001 Lecture Notes in Computer Science  
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
more » ... lement. 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.
doi:10.1007/3-540-44677-x_5 fatcat:mnlvg65fwjftjg3kh7nwgzsy3a