A Map-Reduce-enabled SOLAP cube for large-scale remotely sensed data aggregation
Remote-sensing data aggregation in Spatial OLAP faces big data challenge. A tile-based SOLAP cube model for raster is presented based on parallel map algebra. The model integrates SOLAP with Map-Reduce for accelerating spatio-temporal aggregations. The model shows good scalability along with both increased data and node number. Complexity of the distribution is encapsulated via transparent SOLAP interface.
solap spatio-temporal cube data-intensive computing cybergis environment monitoring