1 research outputs found
NETR-Tree: An Eifficient Framework for Social-Based Time-Aware Spatial Keyword Query
The prevalence of social media and the development of geo-positioning
technology stimulate the growth of location-based social networks (LBSNs). 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 taking
geo-spatial score, keywords similarity, visiting time score, and social
relationship effect into consideration. To tackle STSKQ, we propose a two-layer
hybrid index structure called Network Embedding Time-aware R-tree (NETR-tree).
In user layer, we exploit network embedding strategy to measure relationship
effect in users' relationship network. In location layer, we build a Time-aware
R-tree (TR-tree), which considers spatial objects' spatio-temporal check-in
information. On the basis of NETR-tree, a corresponding query processing
algorithm is presented. Finally, extensive experiments on real-data collected
from two different real-life LBSNs demonstrate the effectiveness and efficiency
of the proposed methods, compared with existing state-of-the-art methods