Zeidan, Ayman and Lagerspetz, Eemil and Zhao, Kai and Nurmi, Petteri Tapio and Tarkoma, Sasu and Vo, Huy T. (2019) GeoMatch : Efficient Large-Scale Map Matching on Apache Spark. In: 2018 IEEE International Conference on Big Data (Big Data) :. IEEE, pp. 384-391. ISBN 9781538650356
GeoMatch_IEEE_BigData_preprint.pdf - Accepted Version
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Abstract
We contribute by developing GeoMatch as a novel, scalable, and efficient big-data pipeline for large-scale map matching on Apache Spark. GeoMatch improves existing spatial big data solutions by utilizing a novel spatial partitioning scheme inspired by Hilbert space-filling curves. Thanks to the partitioning scheme, GeoMatch can effectively balance operations across different processing units and achieve significant performance gains. We demonstrate the effectiveness of GeoMatch through rigorous and extensive benchmarks that consider data sets containing large-scale urban spatial data sets ranging from 166, 253 to 3.78 billion location measurements. Our results show over 17-fold performance improvements compared to previous works while achieving better processing accuracy than current solutions (97.48%).