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LocationSpark: In-memory Distributed Spatial Query Processing and Optimization
2020
Frontiers in Big Data
Due to the ubiquity of spatial data applications and the large amounts of spatial data that these applications generate and process, there is a pressing need for scalable spatial query processing. In this paper, we present new techniques for spatial query processing and optimization in an in-memory and distributed setup to address scalability. More specifically, we introduce new techniques for handling query skew that commonly happens in practice, and minimizes communication costs accordingly.
doi:10.3389/fdata.2020.00030
pmid:33693403
pmcid:PMC7931877
fatcat:onodyye4uzb4letmbpyembq7je