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The performance of distributed computing is usually limited by the heterogeneous nature of distributed systems, data transfer rate and access latency. Techniques such as adaptive process migration, data caching and prefetching were developed to overcome this limitation. However, such techniques require the knowledge of application behavior in order to be effective. In this sense, we intend to propose a new model for application behavior prediction that, by classifying and analyzing application access patterns, is able to predict future application behavior. The model aims to allow a transparent and automatic process behavior extraction, classification and prediction, using a variable set of techniques, including stochastic models and artificial intelligence-based approaches.