NETR-Tree: An Eifficient Framework for Social-Based Time-Aware Spatial Keyword Query

TL;DR

This paper introduces NETR-Tree, a novel hybrid index structure designed to efficiently process social-based, time-aware spatial keyword queries in location-based social networks, considering multiple factors like spatial, temporal, keyword, and social data.

Contribution

The paper proposes a two-layer hybrid index structure, NETR-Tree, combining network embedding and time-aware R-tree to improve query processing in LBSNs.

Findings

NETR-Tree outperforms existing methods in efficiency.

The approach effectively integrates social, spatial, and temporal data.

Experiments validate the method's scalability and accuracy.

Abstract

The development of global positioning system stimulates the popularity of location-based social network (LBSN) services. With a large volume of data containing locations, texts, check-in information, and social relationships, spatial keyword queries in LBSNs have become increasingly complex. In this paper, we identify and solve the Social-based Time-aware Spatial Keyword Query (STSKQ) that returns the top-k objects by considering geo-spatial score, keywords similarity, visiting time score, and social relationship effect. To tackle STSKQ, we propose a two-layer hybrid index structure called Network Embedding Time-aware R-tree (NETR-Tree). In the user layer, we exploit the network embedding strategy to measure the relationship effect in users' relationship network. In the location layer, we build a Time-aware R-tree (TR-tree) considered spatial objects' spatiotemporal check-in information, and present a corresponding query processing algorithm. Finally, extensive experiments on two different real-life LBSNs demonstrate the effectiveness and efficiency of our methods, compared with existing state-of-the-art methods.