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Large spatial data becomes ubiquitous. As a result, it is critical to provide fast, scalable, and high-throughput spatial queries and analytics for numerous applications in location-based services (LBS). Traditional spatial databases and spatial analytics systems are diskbased and optimized for IO efficiency. But increasingly, data are stored and processed in memory to achieve low latency, and CPU time becomes the new bottleneck. We present the Simba (Spatial In-Memory Big data Analytics)
doi:10.1145/2882903.2915237
dblp:conf/sigmod/XieL0LZG16
fatcat:kkus3fprcjevle5qxw7zaw2wtq