TY - GEN
T1 - TrajMesa
T2 - 36th IEEE International Conference on Data Engineering, ICDE 2020
AU - Li, Ruiyuan
AU - He, Huajun
AU - Wang, Rubin
AU - Ruan, Sijie
AU - Sui, Yuan
AU - Bao, Jie
AU - Zheng, Yu
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/4
Y1 - 2020/4
N2 - Trajectory data is very useful for many urban applications. However, due to its spatio-temporal and high-volume properties, it is challenging to manage trajectory data. Existing trajectory data management frameworks suffer from scalability problem, and only support limited trajectory queries. This paper proposes a holistic distributed NoSQL trajectory storage engine, TrajMesa, based on GeoMesa, an open-source indexing toolkit for spatio-temporal data. TrajMesa adopts a novel storage schema, which reduces the storage size tremendously. We also devise novel indexing key designs, and propose a bunch of pruning strategies. TrajMesa can support plentiful queries efficiently, including ID-Temporal query, spatial range query, similarity query, and k-NN query. Experimental results show the powerful query efficiency and scalability of TrajMesa.
AB - Trajectory data is very useful for many urban applications. However, due to its spatio-temporal and high-volume properties, it is challenging to manage trajectory data. Existing trajectory data management frameworks suffer from scalability problem, and only support limited trajectory queries. This paper proposes a holistic distributed NoSQL trajectory storage engine, TrajMesa, based on GeoMesa, an open-source indexing toolkit for spatio-temporal data. TrajMesa adopts a novel storage schema, which reduces the storage size tremendously. We also devise novel indexing key designs, and propose a bunch of pruning strategies. TrajMesa can support plentiful queries efficiently, including ID-Temporal query, spatial range query, similarity query, and k-NN query. Experimental results show the powerful query efficiency and scalability of TrajMesa.
UR - https://www.scopus.com/pages/publications/85085859489
U2 - 10.1109/ICDE48307.2020.00224
DO - 10.1109/ICDE48307.2020.00224
M3 - Conference contribution
AN - SCOPUS:85085859489
T3 - Proceedings - International Conference on Data Engineering
SP - 2002
EP - 2005
BT - Proceedings - 2020 IEEE 36th International Conference on Data Engineering, ICDE 2020
PB - IEEE Computer Society
Y2 - 20 April 2020 through 24 April 2020
ER -