Efficient Top K Temporal Spatial Keyword Search

TL;DR

This paper introduces SSG-tree, a novel index structure that efficiently supports top-k temporal spatial keyword queries considering time, location, and relevance, demonstrated to outperform existing methods.

Contribution

The paper presents the SSG-tree index and an efficient algorithm for top-k temporal spatial keyword search, addressing high-rate data updates and query performance.

Findings

SSG-tree significantly improves query response times.

The method outperforms alternative techniques in real spatial database experiments.

Efficiently handles high-frequency insertions and deletions.

Abstract

Massive amount of data that are geo-tagged and associated with text information are being generated at an unprecedented scale in many emerging applications such as location based services and social networks. Due to their importance, a large body of work has focused on efficiently computing various spatial keyword queries. In this paper,we study the top-$k$ temporal spatial keyword query which considers three important constraints during the search including time, spatial proximity and textual relevance. A novel index structure, namely SSG-tree, to efficiently insert/delete spatio-temporal web objects with high rates. Base on SSG-tree an efficient algorithm is developed to support top-k temporal spatial keyword query. We show via extensive experimentation with real spatial databases that our method has increased performance over alternate techniques