TY - GEN
T1 - Indexing the pickup and drop-off locations of NYC taxi trips in PostgreSQL – Lessons from the road
AU - Yu, Jia
AU - Elsayed, Mohamed
N1 - Funding Information:
This work is supported by the National Science Foundation Grant 1654861.
Publisher Copyright:
© Springer International Publishing AG 2017.
PY - 2017
Y1 - 2017
N2 - In this paper, we present our experience in indexing the drop-off and pick-up locations of taxi trips in New York City. The paper presents a comprehensive experimental analysis of classic and state-of-the-art spatial database indexing schemes. The paper evaluates a popular spatial tree indexing scheme (i.e., GIST-Spatial), a Block Range Index (BRIN-Spatial) provided by PostgreSQL as well as a new indexing scheme, namely Hippo-Spatial. In the experiments, the paper considers five evaluation metrics to compare and contrast the performance of the three indexing schemes: storage overhead, index initialization time, query response time, maintenance overhead, and throughput. Furthermore, the benchmark takes into account parameters that affect the index performance, which include but is not limited to: data size, spatial query selectivity, and spatial area density, The paper finally analyzes the experimental evaluation results and highlights the key insights and lessons learned. The results emphasize the fact that there is no one size that fits all when it comes to indexing massive-scale spatial data. The results also prove that modern database systems can maintain a lightweight index (in terms of storage and maintenance overhead) that is also fast enough for spatial data analytics applications. The source code for the experiments presented in the paper is available here: https://github.com/DataSystemsLab/hippo-postgresql.
AB - In this paper, we present our experience in indexing the drop-off and pick-up locations of taxi trips in New York City. The paper presents a comprehensive experimental analysis of classic and state-of-the-art spatial database indexing schemes. The paper evaluates a popular spatial tree indexing scheme (i.e., GIST-Spatial), a Block Range Index (BRIN-Spatial) provided by PostgreSQL as well as a new indexing scheme, namely Hippo-Spatial. In the experiments, the paper considers five evaluation metrics to compare and contrast the performance of the three indexing schemes: storage overhead, index initialization time, query response time, maintenance overhead, and throughput. Furthermore, the benchmark takes into account parameters that affect the index performance, which include but is not limited to: data size, spatial query selectivity, and spatial area density, The paper finally analyzes the experimental evaluation results and highlights the key insights and lessons learned. The results emphasize the fact that there is no one size that fits all when it comes to indexing massive-scale spatial data. The results also prove that modern database systems can maintain a lightweight index (in terms of storage and maintenance overhead) that is also fast enough for spatial data analytics applications. The source code for the experiments presented in the paper is available here: https://github.com/DataSystemsLab/hippo-postgresql.
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U2 - 10.1007/978-3-319-64367-0_8
DO - 10.1007/978-3-319-64367-0_8
M3 - Conference contribution
AN - SCOPUS:85028450354
SN - 9783319643663
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 145
EP - 162
BT - Advances in Spatial and Temporal Databases - 15th International Symposium, SSTD 2017, Proceedings
A2 - Ku, Wei-Shinn
A2 - Voisard, Agnes
A2 - Chen, Haiquan
A2 - Lu, Chang-Tien
A2 - Ravada, Siva
A2 - Renz, Matthias
A2 - Huang, Yan
A2 - Gertz, Michael
A2 - Tang, Liang
A2 - Zhang, Chengyang
A2 - Hoel, Erik
A2 - Zhou, Xiaofang
PB - Springer Verlag
T2 - 15th International Symposium on Spatial and Temporal Databases, SSTD 2017
Y2 - 21 August 2017 through 23 August 2017
ER -