  We focus in this report on two main axes. The first is dedicated to the study
of the effect of replicas distribution on data grid performances. In this
respect, our main contributions are as follows: 1) An overview of replication
strategies mainly from the viewpoints of the considered parameters in their
associated steps as well as the used metrics in the literature for their
evaluation. 2) A study of the impact of placement strategies on data grid
performance which motivated the analysis of the effect of the replicas
distribution quality on the performance results of replication strategies. 3)
The proposal of new evaluation metrics dedicated to the evaluation of the
distribution quality. 4) The setting of an objective evaluation of replication
strategies which is based on a beforehand assessment of the distribution
quality.
  The second axis is mainly dedicated to exploiting results of data mining
techniques to enhance performances of replication strategies. With respect to
this axis, we mainly concentrate on the following contributions listed below:
1) The study of the strengths and the drawbacks of the main replication
strategies based on data mining techniques and how these latter are applied in
this context. 2) The proposal of a new guideline to data mining application in
the context of data grid replication strategies. 3) The proposal of a new
algorithm for mining maximal frequent correlated patterns. The input of this
algorithm is obtained through a preliminary step focusing on how to adapt the
required grid concepts to the data mining algorithm. 4) The design and the
implementation of a new replication strategy based on a data mining technique,
and more precisely correlated patterns.
