GeoMatch: Efficient Large-Scale Map Matching on Apache Spark

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

Forskningsoutput: Kapitel i bok/rapport/konferenshandlingKonferensbidragVetenskapligPeer review

Sammanfattning

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%).
Originalspråkengelska
Titel på värdpublikationProceedings of the 2018 IEEE International Conference on Big Data
RedaktörerN 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
Antal sidor8
UtgivningsortNew York
FörlagIEEE
Utgivningsdatum2019
Sidor384-391
ISBN (elektroniskt)978-1-5386-5035-6
DOI
StatusPublicerad - 2019
MoE-publikationstypA4 Artikel i en konferenspublikation
EvenemangIEEE International Conference on Big Data - Seattle, Förenta Staterna (USA)
Varaktighet: 10 dec. 201813 dec. 2018

Publikationsserier

NamnIEEE International Conference on Big Data
FörlagIEEE
ISSN (elektroniskt)2639-1589

Vetenskapsgrenar

  • 113 Data- och informationsvetenskap

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