Efficient Processing of Group Planning Queries Over Spatial-Social Networks
Ahmed Al-Baghdadi, Gokarna Sharma, Xiang Lian · IEEE Transactions on Knowledge and Data Engineering · 2020
Recently, location-based social networks, that involve both social and spatial information, have received much attention in many real-world applications such as location-based services (LBS), map utilities, business planning, and so on. In this paper, we seamlessly integrate both social networks and spatial road networks, resulting in a so-calledspatial-social network, and study an important and novel query type, namedgroup planning query over spatial-social networks(GP-SSN), which is very useful for applications such as trip recommendations. In particular, a GP-SSN query retrieves a group of friends with common interests on social networks and a number of spatially closepoints of interest(POIs) on spatial road networks that best match group’s preferences and have the smallest traveling distances to the group. In order to tackle the GP-SSN problem, we design effective pruning methods, matching score pruning, user pruning, and distance pruning, to rule out false alarms of GP-SSN query answers and reduce the problem search space. We also propose effective indexing mechanisms to facilitate the GP-SSN query processing, and develop efficient GP-SSN query answering algorithms via index traversals. Extensive experiments have been conducted to evaluate the efficiency and effectiveness of our proposed GP-SSN query processing approaches.