Digital Library[ Search Result ]
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.
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.
Fast Hilbert R-tree Bulk-loading Scheme using GPGPU
In spatial databases, R tree is one of the most widely used indexing structures and many variants have been proposed for its performance improvement. Among these variants, Hilbert R tree is a representative method using Hilbert curve to process large amounts of data without high cost split techniques to construct the R tree. This Hilbert R tree, however, is hardly applicable to large scale applications in practice mainly due to high pre processing costs and slow bulk load time. To overcome the limitations of Hilbert R tree, we propose a novel approach for parallelizing Hilbert mapping and thus accelerating bulk loading of Hilbert R tree on GPU memory. Hilbert R tree based on GPU improves bulk loading performance by applying the inversed cell method and exploiting parallelism for packing the R tree structure. Our experimental results show that the proposed scheme is up to 45 times faster compared to the traditional CPU based bulk loading schemes.
Search
Journal of KIISE
- ISSN : 2383-630X(Print)
- ISSN : 2383-6296(Electronic)
- KCI Accredited Journal
Editorial Office
- Tel. +82-2-588-9240
- Fax. +82-2-521-1352
- E-mail. chwoo@kiise.or.kr