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An adaptive hierarchical caching scheme for remotely sensed database

With the rapid growth of users and data volume, the state-of-the-art remotely sensed databases have been posed on grand challenges by the online geospatial applications. They cannot provide low-latency retrieval of big remotely sensed data and fail to adapt for various access patterns from concurrent users. We propose an adaptive hierarchical caching scheme called RSCache for remotely sensed database to overcome the deficiencies. The big data are split into large amounts of small tiles and stored as key-value objects in NoSQL database with Hilbert order. Moreover, a distributed object placement model is designed for different access patterns with consideration of spatio-temporal proximity. Besides, RSCache forms a large memory cache by collecting the exclusive memory pools allocated by cluster nodes and provides global unified view of data manipulation from application's perspective. We implement RSCache middleware prototype on scale-out HBase cluster and confirm its effectiveness with comprehensive experiments in real applications.