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Grid computing is moving into two ways. The Computational Grid focuses on reducing execution time of applications that require a great number of computer processing cycles. The Data Grid provides the way to solve large scale data management problems. Data intensive applications such as High Energy Physics and Bioinformatics require both Computational and Data Grid features. Job scheduling in Grid has been mostly discussed from the perspective of computational Grid. However, scheduling on Data Grid is just a recent focus of Grid computing activities. In Data Grid environment, effective scheduling mechanism considering both computational and data storage resources must be provided for large scale data intensive applications. In this paper, we describe new scheduling model that considers both amount of computational resources and data availability in Data Grid environment. We implemented a scheduler, called Chameleon, based on the proposed application scheduling model. Chameleon shows performance improvements in data intensive applications that require both large number of processors and data replication mechanisms. The results achieved from Chameleon are presented.