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千亿体育:香港科技大学张梦璇博士学术报告

来源: 点击: 时间:2023年10月26日 15:26

报告人:张梦璇 香港科技大学(广州)资深研究助理

报告地点: 新校区信息楼535会议室

报告时间: 2023年10月30日(周一)下午3:30

报告题目:Efficient Shortest Path Query Processing in Dynamic Road Networks


个人简介:

Dr. Mengxuan Zhang is a Research Associate at Hong Kong University of Science and Technology. She obtained her PhD (Computer Science) from the University of Queensland in 2021, her Master degree (Control Theory and Control Engineering) from Dalian University of Technology in 2017 and Bachelor degree (Automation) from Wuhan University of Science and Technology in 2014. Her research interests include Spatial-Temporal Database Management, Dynamic Route Planning and Graph Algorithm.


摘要:Shortest path query processing is the fundamental operation for the app-based/ in-car navigation services and the building block of various applications. Even though this query has been extensively studied in the last decades, it is far from enough for the real-life road network applications, which have two characteristics: (1) a huge number of queries are submitted to the servers simultaneously; (2) the traffic in road network keeps evolving all the time. However, the existing algorithms can either adapt to dynamics naturally or process the query efficiently. Therefore, we study how to process shortest path query efficiently in dynamics and propose three types of methods: (1) The first solution is to process the queries by batch with index-free algorithms. The index-free algorithms can deal with dynamics naturally while with relatively slow query processing speed; (2) The second solution is snapshot clustering with index-based algorithms, and we treat the road network as a sequence of snapshots. The index-based algorithms can achieve high query efficiency and support the dynamics; (3) The third solution is index maintenance. We refresh the index incrementally and efficiently in dynamics. The significant of this work is attested by six top-tier conference and journal papers. In addition, multiple future research directions are being explored based on the existing achievements.


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