GeoMatch: Efficient Large-Scale Map Matching on Apache Spark

Ayman Zeidan, Eemil Lagerspetz, Kai Zhao, Petteri Nurmi, Sasu Tarkoma, Huy Vo

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

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 ex-
isting 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%).
Original languageEnglish
Title of host publicationProceedings of the 2018 IEEE International Conference on Big Data
EditorsN Abe, H Liu, C Pu, X Hu, N Ahmed, M Qiao, Y Song, D Kossmann, B Liu, K Lee, J Tang, J He, J Saltz
Number of pages8
Place of PublicationNew York
PublisherIEEE
Publication date2019
Pages384-391
ISBN (Electronic)978-1-5386-5035-6
DOIs
Publication statusPublished - 2019
MoE publication typeA4 Article in conference proceedings
EventIEEE International Conference on Big Data - Seattle, United States
Duration: 10 Dec 201813 Dec 2018

Publication series

NameIEEE International Conference on Big Data
PublisherIEEE
ISSN (Electronic)2639-1589

Fields of Science

  • 113 Computer and information sciences

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