GeoMatch: Efficient Large-Scale Map Matching on Apache Spark

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

Tutkimustuotos: Artikkeli kirjassa/raportissa/konferenssijulkaisussaKonferenssiartikkeliTieteellinenvertaisarvioitu

Kuvaus

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%).
Alkuperäiskielienglanti
OtsikkoProceedings of the 2018 IEEE International Conference on Big Data
ToimittajatN 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
Sivumäärä8
JulkaisupaikkaNew York
KustantajaIEEE
Julkaisupäivä2018
Sivut384-391
ISBN (elektroninen)978-1-5386-5035-6
DOI - pysyväislinkit
TilaJulkaistu - 2018
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisuussa
TapahtumaIEEE International Conference on Big Data - Seattle, Yhdysvallat (USA)
Kesto: 10 joulukuuta 201813 joulukuuta 2018

Julkaisusarja

NimiIEEE International Conference on Big Data
KustantajaIEEE
ISSN (elektroninen)2639-1589

Tieteenalat

  • 113 Tietojenkäsittely- ja informaatiotieteet

Lainaa tätä

Zeidan, A., Lagerspetz, E., Zhao, K., Nurmi, P., Tarkoma, S., & Vo, H. (2018). GeoMatch: Efficient Large-Scale Map Matching on Apache Spark. teoksessa N. 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 (Toimittajat), Proceedings of the 2018 IEEE International Conference on Big Data (Sivut 384-391). (IEEE International Conference on Big Data). New York: IEEE. https://doi.org/10.1109/BigData.2018.8622488
Zeidan, Ayman ; Lagerspetz, Eemil ; Zhao, Kai ; Nurmi, Petteri ; Tarkoma, Sasu ; Vo, Huy. / GeoMatch: Efficient Large-Scale Map Matching on Apache Spark. Proceedings of the 2018 IEEE International Conference on Big Data. Toimittaja / N 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. New York : IEEE, 2018. Sivut 384-391 (IEEE International Conference on Big Data).
@inproceedings{9f7254e8faf140208a8e51bc2ec971f2,
title = "GeoMatch: Efficient Large-Scale Map Matching on Apache Spark",
abstract = "We contribute by developing GeoMatch as anovel, scalable, and efficient big-data pipeline for large-scalemap matching on Apache Spark. GeoMatch improves ex-isting spatial big data solutions by utilizing a novel spatialpartitioning scheme inspired by Hilbert space-filling curves.Thanks to the partitioning scheme, GeoMatch can effectivelybalance operations across different processing units and achievesignificant performance gains. We demonstrate the effectivenessof GeoMatch through rigorous and extensive benchmarks thatconsider data sets containing large-scale urban spatial data setsranging from 166, 253 to 3.78 billion location measurements.Our results show over 17-fold performance improvementscompared to previous works while achieving better processingaccuracy than current solutions (97.48{\%}).",
keywords = "113 Computer and information sciences",
author = "Ayman Zeidan and Eemil Lagerspetz and Kai Zhao and Petteri Nurmi and Sasu Tarkoma and Huy Vo",
year = "2018",
doi = "10.1109/BigData.2018.8622488",
language = "English",
series = "IEEE International Conference on Big Data",
publisher = "IEEE",
pages = "384--391",
editor = "N Abe and H Liu and C Pu and X Hu and N Ahmed and M Qiao and Y Song and D Kossmann and B Liu and K Lee and J Tang and J He and J Saltz",
booktitle = "Proceedings of the 2018 IEEE International Conference on Big Data",
address = "International",

}

Zeidan, A, Lagerspetz, E, Zhao, K, Nurmi, P, Tarkoma, S & Vo, H 2018, GeoMatch: Efficient Large-Scale Map Matching on Apache Spark. julkaisussa N 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 (toim), Proceedings of the 2018 IEEE International Conference on Big Data. IEEE International Conference on Big Data, IEEE, New York, Sivut 384-391, IEEE International Conference on Big Data, Seattle, Yhdysvallat (USA), 10/12/2018. https://doi.org/10.1109/BigData.2018.8622488

GeoMatch: Efficient Large-Scale Map Matching on Apache Spark. / Zeidan, Ayman; Lagerspetz, Eemil; Zhao, Kai; Nurmi, Petteri; Tarkoma, Sasu; Vo, Huy.

Proceedings of the 2018 IEEE International Conference on Big Data. toim. / N 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. New York : IEEE, 2018. s. 384-391 (IEEE International Conference on Big Data).

Tutkimustuotos: Artikkeli kirjassa/raportissa/konferenssijulkaisussaKonferenssiartikkeliTieteellinenvertaisarvioitu

TY - GEN

T1 - GeoMatch: Efficient Large-Scale Map Matching on Apache Spark

AU - Zeidan, Ayman

AU - Lagerspetz, Eemil

AU - Zhao, Kai

AU - Nurmi, Petteri

AU - Tarkoma, Sasu

AU - Vo, Huy

PY - 2018

Y1 - 2018

N2 - We contribute by developing GeoMatch as anovel, scalable, and efficient big-data pipeline for large-scalemap matching on Apache Spark. GeoMatch improves ex-isting spatial big data solutions by utilizing a novel spatialpartitioning scheme inspired by Hilbert space-filling curves.Thanks to the partitioning scheme, GeoMatch can effectivelybalance operations across different processing units and achievesignificant performance gains. We demonstrate the effectivenessof GeoMatch through rigorous and extensive benchmarks thatconsider data sets containing large-scale urban spatial data setsranging from 166, 253 to 3.78 billion location measurements.Our results show over 17-fold performance improvementscompared to previous works while achieving better processingaccuracy than current solutions (97.48%).

AB - We contribute by developing GeoMatch as anovel, scalable, and efficient big-data pipeline for large-scalemap matching on Apache Spark. GeoMatch improves ex-isting spatial big data solutions by utilizing a novel spatialpartitioning scheme inspired by Hilbert space-filling curves.Thanks to the partitioning scheme, GeoMatch can effectivelybalance operations across different processing units and achievesignificant performance gains. We demonstrate the effectivenessof GeoMatch through rigorous and extensive benchmarks thatconsider data sets containing large-scale urban spatial data setsranging from 166, 253 to 3.78 billion location measurements.Our results show over 17-fold performance improvementscompared to previous works while achieving better processingaccuracy than current solutions (97.48%).

KW - 113 Computer and information sciences

U2 - 10.1109/BigData.2018.8622488

DO - 10.1109/BigData.2018.8622488

M3 - Conference contribution

T3 - IEEE International Conference on Big Data

SP - 384

EP - 391

BT - Proceedings of the 2018 IEEE International Conference on Big Data

A2 - Abe, N

A2 - Liu, H

A2 - Pu, C

A2 - Hu, X

A2 - Ahmed, N

A2 - Qiao, M

A2 - Song, Y

A2 - Kossmann, D

A2 - Liu, B

A2 - Lee, K

A2 - Tang, J

A2 - He, J

A2 - Saltz, J

PB - IEEE

CY - New York

ER -

Zeidan A, Lagerspetz E, Zhao K, Nurmi P, Tarkoma S, Vo H. GeoMatch: Efficient Large-Scale Map Matching on Apache Spark. julkaisussa Abe N, Liu H, Pu C, Hu X, Ahmed N, Qiao M, Song Y, Kossmann D, Liu B, Lee K, Tang J, He J, Saltz J, toimittajat, Proceedings of the 2018 IEEE International Conference on Big Data. New York: IEEE. 2018. s. 384-391. (IEEE International Conference on Big Data). https://doi.org/10.1109/BigData.2018.8622488