An Effective Spatio-Temporal Query Framework for Massive Trajectory Data in Urban Computing

With the development of IoT techniques, urban computing has become an emerging topic in academia and industry. The goal of urban computing is to address some issues of urban planning by using the big data generated in urban facilities. The massive trajectory data processing is viewed as an important issue in urban computing. To satisfy the storage and processing requirements of massive trajectory data, a distributed system is usually adopted. However, existing distributed systems face challenges of data locality aware partitioning and various trajectory queries. In this paper, we propose a distributed framework of massive trajectory data analysis based on HBase, to realize spatio-temporal query more effectively. We first design a temporal-based pre-partitioning strategy to improve the performance of data written. Then we develop a Multi-Level Index to speed up the process of spatio-temporal query. Extensive experiments on real trajectory datasets demonstrate that the proposed framework significantly improves efficiency and usability.