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Partially Collective Spatial Keyword Query Processing Based on Spatial Keyword Similarity
Ah Hyun Lee, Sehwa Park, Seog Park
http://doi.org/10.5626/JOK.2021.48.10.1142
Collective spatial keyword queries return Points of Interest (POI), which are close to the query location and contain all the presented set of keywords. However, existing studies only consider a fixed number of query keywords, which is not adequate to satisfy the user. They do not care about the preference of a partial keyword set, and a flexible keyword set needs to be selected for the preference of each POI. We thus propose a new query, called Partially Collective Spatial Keyword Query, which flexibly considers keywords that fit the preference for each POI. Since this query is a combinatorial optimization problem, the query processing time increases rapidly as the number of POIs increases. Therefore, to address these problems, we propose a keyword-based search technique that reduces the overall search space. Furthermore, we propose heuristic techniques, which include the linear search-based terminal node pruning technique, approximation algorithm, and threshold-based pruning technique.
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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