A Framework for Efficient Query Answering on Semantically Heterogeneous Grids

With the rapid growth of Grid computing, more and more data are generated and stored across the grid. To fully utilize the data, efficient data search and query answering mechanism is becoming a very important issue. However, the sheer amount of data and their heterogeneity nature pose challenges that current technology cannot cope with efficiently. In this paper, we propose an efficient query answering solution that integrates topology adaptation, semantic query routing, and view-based caching techniques to reduce bandwidth cost of distributed query processing while allowing efficient evaluation of complex semantic queries over large-scale, fully decentralized, and semantically heterogeneous grids. Simulated experimentations illustrate that our comprehensive query strategies effectively reduce the cost of query evaluation and improves the query performance.