Open AccessHBase-based spatial-temporal index model for trajectory data
Author(s) -
Hong Liu,
Jining Yan,
Xiaohui Huang
Publication year - 2022
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.179
H-Index - 26
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/1004/1/012007
Subject(s) - computer science , trajectory , data mining , query optimization , index (typography) , coding (social sciences) , scheduling (production processes) , volume (thermodynamics) , big data , real time computing , database , mathematical optimization , mathematics , quantum mechanics , statistics , physics , astronomy , world wide web
The development of global positioning technology and the popularization of smart mobile terminals has led to a trend of rapid growth in the data volume and coverage of trajectory data. This type of data has the characteristics of fast update speed, high dimensional characteristics, and a large amount of information that can be mined. Many technology companies will use trajectory data to provide location-based services, such as vehicle scheduling and road condition estimation. However, the storage and query efficiency of massive trajectory data have increasingly become bottlenecks in these applications, especially for large-scale spatiotemporal query scenarios. This paper solves this problem by designing a trajectory data index model based on GeoSOT-ST spatiotemporal coding. Based on this model, the HBase-based trajectory data storage scheme and spatiotemporal range query technology are studied, and MapReduce is used as the calculation engine to complete the query attribute condition filtering in parallel. Comparative experiments prove that the index model proposed in this paper can achieve efficient trajectory data query management.