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Dynamic Clustering in Object-Oriented Databases: An Advocacy for Simplicity
[chapter]
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
doi:10.1007/3-540-44677-x_5
fatcat:mnlvg65fwjftjg3kh7nwgzsy3a