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Grids systems are enormous environments that allow to users to share their resource and collaborate for executing of consumer's job. Recently, the need for interoperability among different grid systems and using online updated grid resource information centers for both market-base grids and non-economic grids has become increased. In this paper we specifically focus on online updating resource information centers to use by local schedulers based on assumed hierarchical model. Moreover, we used knowledge extraction methods to provide some helpful predictions to classifying grid nodes based on job's features. A positive point of this research is that schedulers don't waste extra time for getting up-to-date information of grid nodes. The experimental result show the advantages of our approach compared to other conservative methods, especially due to its ability to predict the behavior of nodes based on comprehensive data tables on each node.