A hybrid index model for efficient spatio-temporal search in HBase

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 (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.