Li Xin, Yu Huayan, Yuan Ligang, Qin Xiaolin
College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China.
State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210023, China.
Sensors (Basel). 2022 Feb 23;22(5):1748. doi: 10.3390/s22051748.
The unprecedented development of Internet of Things (IoT) technology produces humongous amounts of spatio-temporal sensing data with various geometry types. However, processing such datasets is often challenging due to high-dimensional sensor data geometry characteristics, complex anomalistic spatial regions, unique query patterns, and so on. Timely and efficient spatio-temporal querying significantly improves the accuracy and intelligence of processing sensing data. Most existing query algorithms show their lack of supporting spatio-temporal queries and irregular spatial areas. In this paper, we propose two spatio-temporal query optimization algorithms based on SpatialHadoop to improve the efficiency of query spatio-temporal sensing data: (1) spatio-temporal polygon range query (STPRQ), which aims to find all records from a polygonal location in a time interval; (2) spatio-temporal nearest neighbors query (STNNQ), which directly searches the query point's closest neighbors. To optimize the STNNQ algorithm, we further propose an adaptive iterative range optimization algorithm (AIRO), which can optimize the iterative range of the algorithm according to the query time range and avoid querying irrelevant data partitions. Finally, extensive experiments based on trajectory datasets demonstrate that our proposed query algorithms can significantly improve query performance over baseline algorithms and shorten response time by 81% and 35.6%, respectively.
物联网（IoT）技术的空前发展产生了大量具有各种几何类型的时空传感数据。然而，由于高维传感器数据的几何特征、复杂的异常空间区域、独特的查询模式等，处理此类数据集往往具有挑战性。及时高效的时空查询显著提高了处理传感数据的准确性和智能性。大多数现有的查询算法都显示出缺乏对时空查询和不规则空间区域的支持。在本文中，我们提出了两种基于SpatialHadoop的时空查询优化算法，以提高查询时空传感数据的效率：（1）时空多边形范围查询（STPRQ），旨在在一个时间间隔内从多边形位置找到所有记录；（2）时空最近邻查询（STNNQ），直接搜索查询点的最近邻。为了优化STNNQ算法，我们进一步提出了一种自适应迭代范围优化算法（AIRO），它可以根据查询时间范围优化算法的迭代范围，避免查询不相关的数据分区。最后，基于轨迹数据集的大量实验表明，我们提出的查询算法比基线算法能显著提高查询性能，响应时间分别缩短了81%和35.6%。
