Large-Scale Spatial Join Query Processing in Cloud

被引：0

作者：

You, Simin ^{[1
]}

Zhang, Jianting ^{[2
]}

Gruenwald, Le ^{[3
]}

机构：

[1] CUNY, Grad Ctr, Dept Comp Sci, New York, NY 10021 USA

[2] CUNY, Dept Comp Sci, New York, NY 10021 USA

[3] Univ Oklahoma, Dept Comp Sci, Norman, OK 73019 USA

来源：

2015 13TH IEEE INTERNATIONAL CONFERENCE ON DATA ENGINEERING WORKSHOPS (ICDEW) | 2015年

关键词：

Spatial Join; Spark; Impala; Cloud Computing; MAPREDUCE;

D O I：

暂无

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

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 similar to 170 million taxi trips in New York City and similar to 10 million global species occurrences records have demonstrated both efficiency and scalability using Amazon EC2 clusters.

引用

页码：34 / 41

页数：8

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