The execution of data intensive grid applications still raises several questions regarding job scheduling, data migration and replication. The optimization techniques applied by these services significantly determine how fast a job can be executed and how early the user can get the execution results.

In this paper we present strategies for scheduling the execution of data intensive applications. We deem that by taking into account the way applications access their data, the grid middleware can achieve lower response times and earlier execution results. Therefore, we (1) monitor the execution of jobs and gather the necessary resource access information, (2) analyze the compiled information and generate a description of the behavior of the job, and (3) use the generated behavior description to implement optimized scheduling algorithms. This technique can be extremely useful in the case of parameter-sweep applications.