An Evaluation Model for Clustering Strategies in the O 2 Object-Oriented Database

This paper adresses the problem of clustering complex data on disk to minimize the number of I/O in data intensive applications. It describes the clustering strategies adopted in the O 2 system. As clustering depends on both structural aspects (composition hierarchy of the classes) and dynamic aspects (the methods associated with the classes) the paper details a cost model in order to evaluate the beneets of the clustering strategies. This model will permit to automatically derive new clustering strategies. To this end, a derivation algorithm which builds an optimal strategy in linear time is presented.