LocationSpark: In-memory Distributed Spatial Query Processing and Optimization.

ABSTRACT: 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. We propose a distributed query scheduler that uses a new cost model to minimize the cost of spatial query processing. The scheduler generates query execution plans that minimize the effect of query skew. The query scheduler utilizes new spatial indexing techniques based on bitmap filters to forward q

SUBMITTER: Tang M 

PROVIDER: S-EPMC7931877 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature