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GeoMatch: Efficient Large-Scale Map Matching on Apache Spark

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

Published

Standard

GeoMatch: Efficient Large-Scale Map Matching on Apache Spark. / Zeidan, Ayman; Lagerspetz, Eemil; Zhao, Kai et al.
2018 IEEE International Conference on Big Data (Big Data). IEEE, 2019. p. 384-391.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

Harvard

Zeidan, A, Lagerspetz, E, Zhao, K, Nurmi, PT, Tarkoma, S & Vo, HT 2019, GeoMatch: Efficient Large-Scale Map Matching on Apache Spark. in 2018 IEEE International Conference on Big Data (Big Data). IEEE, pp. 384-391. https://doi.org/10.1109/BigData.2018.8622488

APA

Zeidan, A., Lagerspetz, E., Zhao, K., Nurmi, P. T., Tarkoma, S., & Vo, H. T. (2019). GeoMatch: Efficient Large-Scale Map Matching on Apache Spark. In 2018 IEEE International Conference on Big Data (Big Data) (pp. 384-391). IEEE. https://doi.org/10.1109/BigData.2018.8622488

Vancouver

Zeidan A, Lagerspetz E, Zhao K, Nurmi PT, Tarkoma S, Vo HT. GeoMatch: Efficient Large-Scale Map Matching on Apache Spark. In 2018 IEEE International Conference on Big Data (Big Data). IEEE. 2019. p. 384-391 doi: 10.1109/BigData.2018.8622488

Author

Zeidan, Ayman ; Lagerspetz, Eemil ; Zhao, Kai et al. / GeoMatch : Efficient Large-Scale Map Matching on Apache Spark. 2018 IEEE International Conference on Big Data (Big Data). IEEE, 2019. pp. 384-391

Bibtex

@inproceedings{8ee2072abc5c4fc29eb174ba2652e5e7,
title = "GeoMatch: Efficient Large-Scale Map Matching on Apache Spark",
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%).",
author = "Ayman Zeidan and Eemil Lagerspetz and Kai Zhao and Nurmi, {Petteri Tapio} and Sasu Tarkoma and Vo, {Huy T.}",
year = "2019",
month = jan,
day = "24",
doi = "10.1109/BigData.2018.8622488",
language = "English",
pages = "384--391",
booktitle = "2018 IEEE International Conference on Big Data (Big Data)",
publisher = "IEEE",

}

RIS

TY - GEN

T1 - GeoMatch

T2 - Efficient Large-Scale Map Matching on Apache Spark

AU - Zeidan, Ayman

AU - Lagerspetz, Eemil

AU - Zhao, Kai

AU - Nurmi, Petteri Tapio

AU - Tarkoma, Sasu

AU - Vo, Huy T.

PY - 2019/1/24

Y1 - 2019/1/24

N2 - 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%).

AB - 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%).

U2 - 10.1109/BigData.2018.8622488

DO - 10.1109/BigData.2018.8622488

M3 - Conference contribution/Paper

SP - 384

EP - 391

BT - 2018 IEEE International Conference on Big Data (Big Data)

PB - IEEE

ER -