Towards Keyword-Based Geo-Social Group Query Services

Huaijie Zhu, Wei Liu, Jian Yin, Jianliang Xu, Wang-Chien Lee

Abstract: In this article, we study a novel variant of geo-social group queries, namely, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><u>k</u>eyword-based <u>g</u>eo-<u>s</u>ocial <u>g</u>roup</i> ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">KGSG</i> ) queries. Motivated by group-based activity planning, KGSG ensures that the attendees have a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">good social relationship</i> , are <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">close enough to the activity location</i> , and are <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">interested in</i> 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 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Distance Ordering based</i> (Baseline) and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Breadth Distance Ordering with Neighbor Expanding</i> (BDONE). To further improve these two R-tree based algorithms, we propose a new keyword-aware social spatial index, called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SIR-tree</i> , which incorporates spatial, social, and keyword information into an R-tree. The novelty of SIR-tree lies in the idea of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">projecting the social relationships of an LBSN on the spatial layer which also maintains the users’ keyword information</i> , to facilitate efficient KGSG query processing. Accordingly, we develop an efficient algorithm, called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">KGSG by SIR-tree Acceleration</i> (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.