A hybrid index model for efficient spatio-temporal search in HBase
Chengyuan Zhangy, Lei Zhuy, Jun Longy, Shuangqiao Liny, Zhan Yangy,, Wenti Huang

TL;DR
This paper introduces HSTI, a hybrid index structure for HBase that significantly improves the efficiency of spatio-temporal k-nearest neighbors searches by reducing search space and outperforming existing methods.
Contribution
The paper proposes a novel hybrid index structure, HSTI, for HBase that effectively integrates spatial and temporal data to enhance search efficiency in spatio-temporal queries.
Findings
HSTI is three to five times faster than existing techniques.
The index reduces search space by considering both spatial and temporal information.
Experiments on real and synthetic data validate the effectiveness of HSTI.
Abstract
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 (STNNS), 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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsData Management and Algorithms · Advanced Image and Video Retrieval Techniques · Geographic Information Systems Studies
