Classic database indexes (e.g., B+-Tree), though speed up queries, suffer from two main drawbacks: (1) An index usually yields 5% to 15% additional storage overhead which results in non-ignorable dollar cost in big data scenarios especially when …
Hippo is a fast, yet scalable, database indexing approach. It significantly shrinks the index storage and mitigates maintenance overhead without compromising much on the query execution performance.
This paper demonstrates GEOSPARK a cluster computing framework for developing and processing large-scale spatial data analytics programs. GEOSPARK consists of three main layers: Apache Spark Layer, Spatial RDD Layer and Spatial Query Processing …
This paper introduces GeoSpark an in-memory cluster computing framework for processing large-scale spatial data. GeoSpark consists of three layers: Apache Spark Layer, Spatial RDD Layer and Spatial Query Processing Layer. Apache Spark Layer provides …
Apache Sedona is a cluster computing system for processing large-scale spatial data. GeoSpark extends Apache Spark / SparkSQL to efficiently load, process, and analyze large-scale spatial data across machines.