With advances in geo-positioning technologies and geo-location services, there are a rapidly growing massive amount of spatio-temporal data collected in many applications such as location-aware devices and wireless communication, in which an object is described by its spatial location and its timestamp. Consequently, the study of spatio-temporal search which explores both geo-location information and temporal information of the data has attracted significant concern from research organizations and commercial communities. This work study the problem of spatio-temporal \emph{k}-nearest neighbors search (ST$k$NNS), which is fundamental in the spatial temporal queries. Based on HBase, a novel index structure is proposed, called \textbf{H}ybrid \textbf{S}patio-\textbf{T}emporal HBase \textbf{I}ndex (\textbf{HSTI} for short), which is carefully designed and takes both spatial and temporal information into consideration to effectively reduce the search space. Based on HSTI, an efficient algorithm is developed to deal with spatio-temporal \emph{k}-nearest neighbors search. Comprehensive experiments on real and synthetic data clearly show that HSTI is three to five times faster than the state-of-the-art technique.

翻译：随着地理定位技术和地理定位服务的进步,在许多应用中,如位置认知装置和无线通信,收集了大量的时空数据,这些数据以空间位置位置和时间戳来描述物体。因此,探索地理定位信息和数据时间资料的时空搜索研究引起了研究组织和商业界的极大关注。这项工作研究的是空间-时空搜索问题,这是空间时间查询的基础。基于 HBase,提出了一个新的索引结构,称为\ textbf{H}Hybrid\ textb{S}Textb}S}patio-textb{T} 探索地理定位信息和数据的时间信息,它吸引了研究组织和商业界的极大关注。它经过仔细设计,将空间和时间信息都考虑在内,以有效减少搜索空间。基于 HSTI, 高效的合成搜索算法比实际搜索速度快。