TY - GEN
T1 - Multi-source Logistics Data Management Architecture
AU - Qian, Rongtao
AU - Zou, Tao
AU - Mao, Jiali
AU - Zhu, Kaixuan
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - As the logistics platform gained in popularity, extreme volume of logistics data has been generated and is continuously growing in size. It becomes a pain point to efficiently query and analyze for different sources of logistics. However, most of the existing distributed methods aim at managing a single-source data like spatial data or trajectory data and build spatial-temporal indexes to improve query efficiency. Thus it is in urgent need of designing an efficient data management architecture for supporting query or analysis on multi-source logistics data. On the basis of distributed environment, we first split massive logistics data into partitions in terms of time dimension, and apply hash algorithm and broadcast mechanism for each partition to accelerate data fusion. Further, we obtain multi-attribute trajectories by regarding the property of other sources of data as the attributes related to the trajectories, and build a distributed index to proliferate the efficiency of querying for logistics data. Finally, comparative experiments are conducted to demonstrate the advantages of our proposal, and a demo system is built for a logistics platform to showcase the effectiveness of our proposal.
AB - As the logistics platform gained in popularity, extreme volume of logistics data has been generated and is continuously growing in size. It becomes a pain point to efficiently query and analyze for different sources of logistics. However, most of the existing distributed methods aim at managing a single-source data like spatial data or trajectory data and build spatial-temporal indexes to improve query efficiency. Thus it is in urgent need of designing an efficient data management architecture for supporting query or analysis on multi-source logistics data. On the basis of distributed environment, we first split massive logistics data into partitions in terms of time dimension, and apply hash algorithm and broadcast mechanism for each partition to accelerate data fusion. Further, we obtain multi-attribute trajectories by regarding the property of other sources of data as the attributes related to the trajectories, and build a distributed index to proliferate the efficiency of querying for logistics data. Finally, comparative experiments are conducted to demonstrate the advantages of our proposal, and a demo system is built for a logistics platform to showcase the effectiveness of our proposal.
KW - Data fusion
KW - Multi-attributes trajectories
KW - Spatio-temporal attribute index
UR - https://www.scopus.com/pages/publications/85142686230
U2 - 10.1007/978-3-031-20891-1_46
DO - 10.1007/978-3-031-20891-1_46
M3 - 会议稿件
AN - SCOPUS:85142686230
SN - 9783031208904
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 641
EP - 649
BT - Web Information Systems Engineering – WISE 2022 - 23rd International Conference, Proceedings
A2 - Chbeir, Richard
A2 - Huang, Helen
A2 - Silvestri, Fabrizio
A2 - Manolopoulos, Yannis
A2 - Zhang, Yanchun
A2 - Zhang, Yanchun
PB - Springer Science and Business Media Deutschland GmbH
T2 - 23rd International Conference on Web Information Systems Engineering, WISE 2021
Y2 - 1 November 2022 through 3 November 2022
ER -