@inproceedings{24662884222042a4863399c99bbf2404,
title = "Simba: Spatial in-memory big data analysis",
abstract = "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.",
author = "Dong Xie and Feifei Li and Bin Yao and Gefei Li and Zhongpu Chen and Liang Zhou and Minyi Guo",
note = "Publisher Copyright: {\textcopyright} 2016 ACM.; 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2016 ; Conference date: 31-10-2016 Through 03-11-2016",
year = "2016",
month = oct,
day = "31",
doi = "10.1145/2996913.2996935",
language = "English (US)",
series = "GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems",
publisher = "Association for Computing Machinery",
editor = "Matthias Renz and Mohamed Ali and Shawn Newsam and Matthias Renz and Siva Ravada and Goce Trajcevski",
booktitle = "24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2016",
}