计算机科学与探索2025，Vol.19Issue(8)：2001-2023,23.DOI:10.3778/j.issn.1673-9418.2411033
时空数据查询技术研究综述
Review of Research on Spatio-Temporal Data Query Technologies
摘要
Abstract
With the rapid development and application of modern information technology,the scale of spatio-temporal data has grown rapidly.These data exhibit characteristics such as massive aggregation,high dimensionality,heterogeneity,and dynamic complexity.In recent years,spatio-temporal query technologies based on spatio-temporal data have been widely researched and applied,making the effective storage,management,and querying of these data a key focus of research.This paper provides an overview of relevant query technologies for spatio-temporal data,starting with the basic concepts related to spatio-temporal data,systematically explaining the current mainstream spatio-temporal query processing models,including various types such as range queries,K-nearest neighbor queries,and reverse K-nearest neighbor queries.Subsequently,different spatio-temporal indexing techniques are introduced,including trajectory-based indexing structures,sampling-based indexing,and other innovative indexing methods.At the same time,querying methods that integrate other technologies are analyzed,mainly including spatio-temporal textual queries,semantic approximate trajectory queries,and parallel and distributed queries.These technologies not only enhance the diversity and accuracy of spatio-temporal queries but also effectively handle large-scale spatio-temporal data.Finally,the future development directions of spatio-temporal query technologies are discussed,including the visualization of query results,privacy protection,and new indexing structures that integrate machine learning,providing new ideas and challenges for the efficient utilization of spatio-temporal data.关键词
时空数据/查询处理/索引技术/时空-文本/语义近似/分布式Key words
spatio-temporal data/query processing/indexing techniques/spatio-temporal textual/semantic approxima-tion/distributed分类
信息技术与安全科学引用本文复制引用
孟祥福,翁雪,徐永杰..时空数据查询技术研究综述[J].计算机科学与探索,2025,19(8):2001-2023,23.基金项目
国家自然科学基金(61772249).This work was supported by the National Natural Science Foundation of China(61772249). （61772249）
