Simba: Spatial In-Memory Big Data Analysis

被引：10

作者：

Xie, Dong ^{[1
]}

Li, Feifei ^{[1
]}

Yao, Bin ^{[2
]}

Li, Gefei ^{[2
]}

Chen, Zhongpu ^{[2
]}

Zhou, Liang ^{[2
]}

Guo, Minyi ^{[2
]}

机构：

[1] Univ Utah, Salt Lake City, UT 84112 USA

[2] Shanghai Jiao Tong Univ, Shanghai 200030, Peoples R China

来源：

24TH ACM SIGSPATIAL INTERNATIONAL CONFERENCE ON ADVANCES IN GEOGRAPHIC INFORMATION SYSTEMS (ACM SIGSPATIAL GIS 2016) | 2016年

基金：

美国国家科学基金会;

关键词：

Simba; Spatial data anlaysis; Big data; Distributed system;

D O I：

10.1145/2996913.2996935

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

We present the Simba ( Spatial In-Memory Big data Analytics) system, which offers scalable and efficient in-memory spatial query processing and analytics for big spatial data. Simba natively extends the Spark SQL engine to support rich spatial queries and analytics through both SQL and DataFrame API. It enables the construction of indexes over RDDs inside the engine in order to work with big spatial data and complex spatial operations. Simba also comes with an effective query optimizer, which leverages its indexes and novel spatial-aware optimizations, to achieve both low latency and high throughput in big spatial data analysis. This demonstration proposal describes key ideas in the design of Simba, and presents a demonstration plan.

引用

页数：4

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