TY - GEN
T1 - An evaluation of modern spatial libraries
AU - Pandey, Varun
AU - van Renen, Alexander
AU - Kipf, Andreas
AU - Kemper, Alfons
N1 - Publisher Copyright:
© Springer Nature Switzerland AG 2020.
PY - 2020
Y1 - 2020
N2 - Applications such as Uber, Yelp, and Tinder rely on spatial data or locations from their users. These applications and services either build their own spatial data management systems or rely on existing solutions. The JTS Topology Suite (JTS), its C++ port GEOS, Google S2, ESRI Geometry API, and Java Spatial Index (JSI) are among the spatial processing libraries that these systems build upon. Applications and services depend on the indexing capabilities available in such libraries for high-performance spatial query processing. However, limited prior work has empirically compared these libraries. Herein, we compare these libraries qualitatively and quantitatively based on four popular spatial queries and using two real-world datasets. We also compare a lesser known library (jvptree) which utilizes Vantage Point Trees. In addition to performance evaluation, we also analyzed the construction time, and space overhead, and identified the strengths and weaknesses of each libraries and their underlying index structures. Our results demonstrate that there are vast differences in space consumption (up to 9.8 x), construction time (up to 5 x), and query runtime (up to 54 x) between the libraries evaluated.
AB - Applications such as Uber, Yelp, and Tinder rely on spatial data or locations from their users. These applications and services either build their own spatial data management systems or rely on existing solutions. The JTS Topology Suite (JTS), its C++ port GEOS, Google S2, ESRI Geometry API, and Java Spatial Index (JSI) are among the spatial processing libraries that these systems build upon. Applications and services depend on the indexing capabilities available in such libraries for high-performance spatial query processing. However, limited prior work has empirically compared these libraries. Herein, we compare these libraries qualitatively and quantitatively based on four popular spatial queries and using two real-world datasets. We also compare a lesser known library (jvptree) which utilizes Vantage Point Trees. In addition to performance evaluation, we also analyzed the construction time, and space overhead, and identified the strengths and weaknesses of each libraries and their underlying index structures. Our results demonstrate that there are vast differences in space consumption (up to 9.8 x), construction time (up to 5 x), and query runtime (up to 54 x) between the libraries evaluated.
UR - http://www.scopus.com/inward/record.url?scp=85092076907&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-59416-9_46
DO - 10.1007/978-3-030-59416-9_46
M3 - Conference contribution
AN - SCOPUS:85092076907
SN - 9783030594152
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 711
EP - 727
BT - Database Systems for Advanced Applications - 25th International Conference, DASFAA 2020, Proceedings
A2 - Nah, Yunmook
A2 - Cui, Bin
A2 - Lee, Sang-Won
A2 - Yu, Jeffrey Xu
A2 - Moon, Yang-Sae
A2 - Whang, Steven Euijong
PB - Springer Science and Business Media Deutschland GmbH
T2 - 25th International Conference on Database Systems for Advanced Applications, DASFAA 2020
Y2 - 24 September 2020 through 27 September 2020
ER -