HOC-Tree: A Novel Index for efficient Spatio-temporal Range Search

TL;DR

This paper introduces HOC-Tree, an innovative index structure combining Hilbert curve and OC-Tree concepts, designed to efficiently handle spatio-temporal range searches in large datasets.

Contribution

The paper presents the HOC-Tree index, a novel data structure that effectively integrates spatial and temporal data for improved search performance.

Findings

HOC-Tree outperforms existing methods in efficiency.

Experiments show significant speedup on real and synthetic data.

The approach effectively handles large-scale spatio-temporal queries.

Abstract

With the rapid development of mobile computing and Web services, a huge amount of data with spatial and temporal information have been collected everyday by smart mobile terminals, in which an object is described by its spatial information and temporal information. Motivated by the significance of spatio-temporal range search and the lack of efficient search algorithm, in this paper, we study the problem of spatio-temporal range search (STRS), a novel index structure is proposed, called HOC-Tree, which is based on Hilbert curve and OC-Tree, and takes both spatial and temporal information into consideration. Based on HOC-Tree, we develop an efficient algorithm to solve the problem of spatio-temporal range search. Comprehensive experiments on real and synthetic data demonstrate that our method is more efficient than the state-of-the-art technique.