A Survey on Big Data for Trajectory Analytics Trajectory data allow the study of the behavior of moving objects, from humans to animals. Wireless communication, mobile devices, and technologies such as Global Positioning System (GPS) have contributed to the growth of the trajectory research field. With the considerable growth in the volume of trajectory data, storing such data into Spatial Database Management Systems (SDBMS) has become challenging. Hence, Spatial Big Data emerges as a data management technology for indexing, storing, and retrieving large volumes of spatiotemporal data. A Data Warehouse (DW) is one of the premier Big Data analysis and complex query processing infrastructures. Trajectory Data Warehouses (TDW) emerge as a DW dedicated to trajectory data analysis. A list and discussions on problems that use TDW and forward directions for the works in this field are the primary goals of this survey. This article collected stateoftheart on Big Data trajectory analytics. Understanding how the research in trajectory data are being conducted, what main techniques have been used, and how they can be embedded in an Online Analytical Processing (OLAP) architecture can enhance the efficiency and development of decisionmaking systems that deal with trajectory data.

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.