Towards Keyword-Based Geo-Social Group Query Services

In this article, we study a novel variant of geo-social group queries, namely,
keyword-based geo-social group
(
KGSG
) queries. Motivated by group-based activity planning, KGSG ensures that the attendees have a
good social relationship
, are
close enough to the activity location
, and are
interested in
the activity. Efficient processing of the KGSG query is very challenging as the problem is NP-hard. To address the challenge, we first propose two R-tree based algorithms, namely
Distance Ordering based
(Baseline) and
Breadth Distance Ordering with Neighbor Expanding
(BDONE). To further improve these two R-tree based algorithms, we propose a new keyword-aware social spatial index, called
SIR-tree
, which incorporates spatial, social, and keyword information into an R-tree. The novelty of SIR-tree lies in the idea of
projecting the social relationships of an LBSN on the spatial layer which also maintains the users’ keyword information
, to facilitate efficient KGSG query processing. Accordingly, we develop an efficient algorithm, called
KGSG by SIR-tree Acceleration
(KGSG-SIR), which exploits SIR-tree to accelerate query processing of KGSG. We conduct an extensive performance evaluation using four real datasets to validate our ideas and the proposed algorithms. The experimental result shows that the KGSG-SIR algorithm outperforms the two algorithms significantly.