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An Effective High-Performance Multiway Spatial Join Algorithm with Spark

1
Institute of Geographic Information Science, School of Earth Sciences, Zhejiang University, Hangzhou 310027, China
2
Department of Geography, Kent State University, Kent, OH 44240, USA
*
Author to whom correspondence should be addressed.
Academic Editor: Wolfgang Kainz
ISPRS Int. J. Geo-Inf. 2017, 6(4), 96; https://doi.org/10.3390/ijgi6040096
Received: 29 December 2016 / Revised: 10 March 2017 / Accepted: 22 March 2017 / Published: 26 March 2017
Multiway spatial join plays an important role in GIS (Geographic Information Systems) and their applications. With the increase in spatial data volumes, the performance of multiway spatial join has encountered a computation bottleneck in the context of big data. Parallel or distributed computing platforms, such as MapReduce and Spark, are promising for resolving the intensive computing issue. Previous approaches have focused on developing single-threaded join algorithms as an optimizing and partition strategy for parallel computing. In this paper, we present an effective high-performance multiway spatial join algorithm with Spark (MSJS) to overcome the multiway spatial join bottleneck. MSJS handles the problem through cascaded pairwise join. Using the power of Spark, the formerly inefficient cascaded pairwise spatial join is transformed into a high-performance approach. Experiments using massive real-world data sets prove that MSJS outperforms existing parallel approaches of multiway spatial join that have been described in the literature. View Full-Text
Keywords: multiway spatial join; parallel computing; spark; geocomputation performance multiway spatial join; parallel computing; spark; geocomputation performance
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Du, Z.; Zhao, X.; Ye, X.; Zhou, J.; Zhang, F.; Liu, R. An Effective High-Performance Multiway Spatial Join Algorithm with Spark. ISPRS Int. J. Geo-Inf. 2017, 6, 96.

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ISPRS Int. J. Geo-Inf., EISSN 2220-9964, Published by MDPI AG
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