Abstract
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 k nearest neighbors query (STkNNQ), which directly searches the query point’s k closest neighbors. To optimize the STkNNQ 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.
Highlights
With the development of Internet of Things (IoT) technology and the proliferation of mobile smart devices, a large amount of spatio-temporal sensing data have been generated continuously by diverse applications, such as GNSS-enabled mobile devices [1,2], urban traffic [3,4], satellites, and various sensing devices [5,6]
We propose an adaptive iterative range optimization algorithm (AIRO) considering data distribution, which can jointly consider the characteristics of data distribution and spatio-temporal query range and improve the overall stability for the spatio-temporal k nearest neighbors query (STkNNQ) algorithm
In order to test the effectiveness of the AIRO algorithm, we conduct the following experiments to verify the influence of the range radius factor β on the number of query data partitions and the response time of STkNNQ algorithm
Summary
With the development of Internet of Things (IoT) technology and the proliferation of mobile smart devices, a large amount of spatio-temporal sensing data have been generated continuously by diverse applications, such as GNSS-enabled mobile devices [1,2], urban traffic [3,4], satellites, and various sensing devices [5,6]. The research [21,22,23] only supports the rectangular spatial model, which rarely pays attention to the problem of range query under complex irregular polygonal shapes (such as city boundaries and delivery areas) These regions usually have characteristics such as large boundaries and irregular shapes and often require a large number of vertices to be accurately represented in a vector-based form; (3) The efficiency of the spatio-temporal query needs to be further improved and optimized. They are not optimized to deal with large-volume spatio-temporal data covering high-dimensional features with high performance Motivated by these observations, in order to consider the multidimensional spatiotemporal query, support the expansion of complex spatial region models, and further improve the query response performance of massive data, we propose two spatio-temporal. A possible explanation is that their indexes are only for processing spatial operations and cannot identify the characteristics of spatiotemporal data
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