Querying Massive Trajectories by Path on the Cloud

Ruiyuan Li, Sijie Ruan, Jie Bao, Yanhua Li, Yingcai Wu, Yu Zheng

SIGSPATIAL 2017 |

Published by ACM

A path query aims to find the trajectories that pass a given sequence of connected road segments within a time period. It is very useful in many urban applications, e.g., 1) traffic modeling, 2) frequent path mining, and 3) traffic anomaly detection. Existing solutions for path query are implemented based on single machines, which are not efficient for the following tasks: 1) indexing large-scale historical data; 2) handling real-time trajectory updates; and 3) processing concurrent path queries. In this paper, we design and implement a cloud-based path query processing framework based on Microsoft Azure. We modify the suffix tree structure to index the trajectories using Azure Table. The proposed system consists of two main parts: 1) backend processing, which performs the pre-processing and suffix index building with distributed computing platform (i.e., Storm) used to efficiently handle massive real-time trajectory updates; and 2) query processing, which answers path queries using Azure Storm to improve efficiency and overcome the I/O bottleneck. We evaluate the performance of our proposed system based on a real taxi dataset from Guiyang, China.