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The STARK Framework for Spatio-Temporal Data Analytics on Spark

Author:
Hagedorn, Stefan [DBLP] ;
Götze, Philipp [DBLP] ;
Sattler, Kai-Uwe [DBLP]
Abstract
Big Data sets can contain all types of information: from server log files to tracking information of mobile users with their location at a point in time. Apache Spark has been widely accepted for Big Data analytics because of its very fast processing model. However, Spark has no native support for spatial or spatio-temporal data. Spatial filters or joins using, e.g., a contains predicate are not supported and would have to be implemented ine ciently by the users. Also, Spark cannot make use of, e.g., spatial distribution for optimal partitioning. Here we present our STARK framework that adds spatio-temporal support to Spark. It includes spatial partitioners, different modes for indexing, as well as filter, join, and clustering operators. In contrast to existing solutions, STARK integrates seamlessly into any (Scala) Spark program and provides more flexible and comprehensive operators. Furthermore, our experimental evaluation shows that our implementation outperforms existing solutions.
  • Citation
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Hagedorn, S., Götze, P. & Sattler, K.-U., (2017). The STARK Framework for Spatio-Temporal Data Analytics on Spark. In: Mitschang, B., Nicklas, D., Leymann, F., Schöning, H., Herschel, M., Teubner, J., Härder, T., Kopp, O. & Wieland, M. (Hrsg.), Datenbanksysteme für Business, Technologie und Web (BTW 2017). Gesellschaft für Informatik, Bonn. (S. 123-142).
@inproceedings{mci/Hagedorn2017,
author = {Hagedorn, Stefan AND Götze, Philipp AND Sattler, Kai-Uwe},
title = {The STARK Framework for Spatio-Temporal Data Analytics on Spark},
booktitle = {Datenbanksysteme für Business, Technologie und Web (BTW 2017)},
year = {2017},
editor = {Mitschang, Bernhard AND Nicklas, Daniela AND Leymann, Frank AND Schöning, Harald AND Herschel, Melanie AND Teubner, Jens AND Härder, Theo AND Kopp, Oliver AND Wieland, Matthias} ,
pages = { 123-142 },
publisher = {Gesellschaft für Informatik, Bonn},
address = {}
}
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More Info

ISBN: 978-3-88579-659-6
ISSN: 1617-5468
xmlui.MetaDataDisplay.field.date: 2017
Language: en (en)
Content Type: Text/Conference Paper
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  • P265 - BTW2017 - Datenbanksysteme für Business, Technologie und Web [56]

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Diese Digital Library basiert auf DSpace.

 

 


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Gesellschaft für Informatik e.V. (GI), Kontakt: Geschäftsstelle der GI
Diese Digital Library basiert auf DSpace.