@article{MA3895C70,
title = "Partially Collective Spatial Keyword Query Processing Based on Spatial Keyword Similarity",
journal = "Journal of KIISE, JOK",
year = "2021",
issn = "2383-630X",
doi = "10.5626/JOK.2021.48.10.1142",
author = "Ah Hyun Lee,Sehwa Park,Seog Park",
keywords = "query processing,spatial database,spatial keyword query,collective spatial keyword query",
abstract = "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."
}