Name
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%). |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/BigData.2018.8622488 | 2018 IEEE International Conference on Big Data (Big Data) |
Keywords | Field | DocType |
Big Data,Spatial Data Analysis,Spatial Partitioning,Performance,Query Processing,Spark | Scale (map),Spatial analysis,Space partitioning,Data mining,Data set,Spark (mathematics),Computer science,Big data,Map matching,Scalability | Conference |
ISSN | ISBN | Citations |
2639-1589 | 978-1-5386-5036-3 | 0 |
PageRank | References | Authors |
0.34 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ayman Zeidan | 1 | 0 | 0.34 |
| Eemil Lagerspetz | 2 | 427 | 29.56 |
| Kai Zhao | 3 | 104 | 13.74 |
| Petteri Nurmi | 4 | 621 | 57.08 |
| Sasu Tarkoma | 5 | 1312 | 125.76 |
| Huy T. Vo | 6 | 1035 | 61.10 |