Data integration has evolved to provide efficient data management across distributed and heterogeneous data sources in grid environment. However, existing works in data integration consider little knowledge about the above applications. In such settings, queries from the same application are processed independently.

In this paper, application properties are noticed in order to improve query performance for data integration. We present a general-purpose and wellmodular architecture for addressing data integration in grid (DIG) environment first, the modules of which can be flexibly deployed to adapt specific application. Moreover, special attention has been paid on query processing to accommodate application workflow with the component of Query Flow Processor. It introduces multi-query optimization techniques to speed up overall response time for DIG.