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Single Group Collective Trip Planning Query Processing Using G-tree Index Structures on Road Networks
http://doi.org/10.5626/JOK.2020.47.5.513
In this paper, we discuss Single Group Collective Trip Planning (SGCTP) queries that minimize the overall travel cost in location-based ride sharing services. The SGCTP queries identify a meeting point that minimizes the overall cost of such a trip when a group of users are gathered at a particular point and travel to the destination using one vehicle. Although many researches on collective trip planning queries have been conducted, there is a problem that the query performance is effective only in a specific situation. So, we introduce a baseline method of the SGCTP queries and then, propose an effective pruning technique with a G-tree index structure. Additionally, we analyze that the limitations of the previous studies, and experimental results show that the proposed pruning technique can obtain the optimal query result without being affected by the limitations of the previous studies.
Reverse Collective Spatial Keyword Queries based on G-tree for Road Networks
http://doi.org/10.5626/JOK.2019.46.5.478
With the proliferation of mobile devices and social network services, spatial keyword queries have become a hot research topic. Previous works have focused on collective spatial keyword queries (CSKQs), which find a set of objects that covers the queried keywords and is close to the query location. In addition, analyzing the correlation between two objects has been studied extensively for real-world applications, such as location recommendations, personalized advertisements, and online social marketing services. CSKQ is suitable for supporting these services because it returns a correlated set of objects. However, the existing studies on CSKQ have focused only on the users’ perspective, despite the fact that such applications require the objects’ perspective. To address this problem, we propose a novel spatial keyword query (reverse collective spatial keyword query, RCSKQ) and a query processing technique based on the road network environment with a G-tree index structure.
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