Large-scale spatial join query processing in Cloud

Author

Simin You ; Jianting Zhang ; Gruenwald, Le

Author_Institution

Dept. of Comput. Sci., CUNY Grad. Center, New York, NY, USA

fYear

2015

fDate

13-17 April 2015

Firstpage

34

Lastpage

41

Abstract

The rapidly increasing amount of location data available in many applications has made it desirable to process their large-scale spatial queries in Cloud for performance and scalability. We report our designs and implementations of two prototype systems that are ready for Cloud deployments: SpatialSpark based on Apache Spark and ISP-MC based on Cloudera Impala. Both systems support indexed spatial joins based on point-in-polygon test and point-to-polyline distance computation. Experiments on the pickup locations of ~170 million taxi trips in New York City and ~10 million global species occurrences records have demonstrated both efficiency and scalability using Amazon EC2 clusters.

Keywords

cloud computing; geographic information systems; query processing; Amazon EC2 cluster; Apache Spark; Cloudera Impala; ISP-MC; SpatialSpark; cloud computing; cloud deployment; indexed spatial join; large-scale spatial join query processing; large-scale spatial query; location data; point-in-polygon test; point-to-polyline distance computation; taxi trip; Data processing; Filtering; Hardware; Query processing; Scalability; Sparks; Spatial databases; Cloud Computing; Impala; Spark; Spatial Join;

fLanguage

English

Publisher

ieee

Conference_Titel

Data Engineering Workshops (ICDEW), 2015 31st IEEE International Conference on

Conference_Location

Seoul

Type

conf

DOI

10.1109/ICDEW.2015.7129541

Filename

7129541