  This paper explores the effect that changing access patterns has on the
performance of database management systems. Changes in access patterns play an
important role in determining the efficiency of key performance optimization
techniques, such as dynamic clustering, prefetching, and buffer replacement.
However, all existing benchmarks or evaluation frameworks produce static access
patterns in which objects are always accessed in the same order repeatedly.
Hence, we have proposed the Dynamic Evaluation Framework (DEF) that simulates
access pattern changes using configurable styles of change. DEF has been
designed to be open and fully extensible (e.g., new access pattern change
models can be added easily). In this paper, we instantiate DEF into the Dynamic
object Evaluation Framework (DoEF) which is designed for object databases,
i.e., object-oriented or object-relational databases such as multi-media
databases or most XML databases.The capabilities of DoEF have been evaluated by
simulating the execution of four different dynamic clustering algorithms. The
results confirm our analysis that flexible conservative re-clustering is the
key in determining a clustering algorithm's ability to adapt to changes in
access pattern. These results show the effectiveness of DoEF at determining the
adaptability of each dynamic clustering algorithm to changes in access pattern
in a simulation environment. In a second set of experiments, we have used DoEF
to compare the performance of two real-life object stores : Platypus and SHORE.
DoEF has helped to reveal the poor swapping performance of Platypus.
