MovCloud: A CloudEnabled Framework to Analyse Movement Behaviors Understanding human interests and intents from movement data are fundamental challenges for any locationbased service. With the pervasiveness of sensor embedded smartphones and wireless networks and communication, the availability of spatiotemporal mobility trace (timestamped location information) is increasingly growing. Analysing these huge amount of mobility data is another major concern. This paper proposes a cloudbased framework named MovCloud to efficiently manage and analyse mobility data. Specifically, the framework presents a hierarchical indexing schema to store trajectory data in different spatiotemporal resolution, clusters the trajectories based on semantic movement behaviour instead of only raw latitude, longitude point and resolves mobility queries using MapReduce paradigm. MovCloud is implemented over Google Cloud Platform (GCP) and an extensive set of experiments on reallife data yield the effectiveness of the proposed framework. MovCloud has achieved  28% better clustering accuracy and also executed three times faster than the baseline methods.

An Effective SpatioTemporal 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 spatiotemporal query more effectively. We first design a temporalbased prepartitioning strategy to improve the performance of data written. Then we develop a MultiLevel Index to speed up the process of spatiotemporal query. Extensive experiments on real trajectory datasets demonstrate that the proposed framework significantly improves efficiency and usability.