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Cluster computing framework for processing large-scale geospatial data
...According to our benchmark and third-party research papers, Sedona has 50% less peak memory consumption than other Spark-based geospatial data systems for large-scale in-memory query processing. Sedona offers Scala, Java, Spatial SQL, Python, and R APIs and integrates them into underlying system kernels with care. You can simply create spatial analytics and data mining applications and run them in any cloud environments.
An open source spatio-temporal data mining library
Current functions:
1. The General Association Rule Mining Framework(GARMF) library, which support mining association rules from transactions(boolean, weighted, fuzzy), spatial datasets (vector and raster) and spatio-temporal datasets (raster snapshots). Besides it support incremental mining.
2. Rule Filtering Library (RFL), a library for rule evaluation.
3. Besides, DAP-Shell, a GUI shell for GARMF and RFL, will be provided.