Accelerating FineGrained SpatialTextual Trajectory Similarity Joins with GPGPUs The increasing volume of spatialtextual data generated from checkins and reviews facilitates many practical applications. In these applications, similarity joins are the basic operation which further supports a wide range of similarity queries. However, this computeintensive join operation calls for more efficient and effective algorithm to support realtime queries. Exploiting general purpose GPUs (GPGPUs) is one natural solution. Nevertheless, existing join algorithms utilizing GPGPU lack considerations on unique features of spatialtextual trajectories, especially the textual domain, thus resulting in poor performance. In this paper, we first propose a baseline spatialtextual trajectory similarity join algorithm, which is essentially based on a traditional algorithm for relational joins on GPGPUs. Then, by analyzing the bottleneck of this baseline, we further develop a finegrained join algorithm which consists of memory access techniques and optimizations. We propose a strategy of flip to ensure coalesced memory accesses, and a batch scheduler for dividing the whole task into batches to fit the global memory while alleviating imbalanced throughput. Extensive experiments have been conducted over reallife datasets and the results show that our finegrained algorithm performs the best and reduces the latency effectively.

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.