TY - GEN
T1 - Dynamic Hub Labeling for Road Networks
AU - Zhang, Mengxuan
AU - Li, Lei
AU - Hua, Wen
AU - Mao, Rui
AU - Chao, Pingfu
AU - Zhou, Xiaofang
N1 - Funding Information:
ACKNOWLEDGMENT This research is supported by the Australian Research Council (DP200103650 and LP180100018), NSFC 62072311, GBABRF 2020B1515120028 and NSFC U2001212. REFERENCES
Publisher Copyright:
© 2021 IEEE.
PY - 2021/4
Y1 - 2021/4
N2 - Shortest path finding is the building block of various applications in road networks and the index-based algorithms, especially hub labeling, can boost the query performance dramatically. However, the traffic condition keeps changing in real life, making the pre-computed index unable to answer the query correctly. In this work, we adopt the state-of-the-art tree decomposition-based hub labeling as the underlying index, and design efficient algorithms to incrementally maintain the index. Specifically, we first analyze the structural stability of the index in dynamic road networks which enables us to concentrate on label value maintenance. We then introduce the minimum weight property and minimum distance property to guarantee the index correctness without graph traversal. Moreover, we propose the star-centric paradigm for tracing index change and design various pruning techniques to further accelerate the index maintenance. Finally, we extend our algorithms to batch mode for shared computation, extend to structural maintenance for full types of update, and generalize to all kinds of TDHL. Our experimental results validate the superiority of our proposals over existing solutions on both index maintenance and query processing.
AB - Shortest path finding is the building block of various applications in road networks and the index-based algorithms, especially hub labeling, can boost the query performance dramatically. However, the traffic condition keeps changing in real life, making the pre-computed index unable to answer the query correctly. In this work, we adopt the state-of-the-art tree decomposition-based hub labeling as the underlying index, and design efficient algorithms to incrementally maintain the index. Specifically, we first analyze the structural stability of the index in dynamic road networks which enables us to concentrate on label value maintenance. We then introduce the minimum weight property and minimum distance property to guarantee the index correctness without graph traversal. Moreover, we propose the star-centric paradigm for tracing index change and design various pruning techniques to further accelerate the index maintenance. Finally, we extend our algorithms to batch mode for shared computation, extend to structural maintenance for full types of update, and generalize to all kinds of TDHL. Our experimental results validate the superiority of our proposals over existing solutions on both index maintenance and query processing.
UR - http://www.scopus.com/inward/record.url?scp=85112868831&partnerID=8YFLogxK
U2 - 10.1109/ICDE51399.2021.00036
DO - 10.1109/ICDE51399.2021.00036
M3 - Conference article published in proceeding or book
AN - SCOPUS:85112868831
T3 - Proceedings - International Conference on Data Engineering
SP - 336
EP - 347
BT - Proceedings - 2021 IEEE 37th International Conference on Data Engineering, ICDE 2021
PB - IEEE Computer Society
T2 - 37th IEEE International Conference on Data Engineering, ICDE 2021
Y2 - 19 April 2021 through 22 April 2021
ER -