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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.
A Predictive Query Processing Method Considering the Movement of both a User and Objects
http://doi.org/10.5626/JOK.2018.45.12.1302
Recently, with the increase in use of mobile devices such as smart phones and tablet PCs with GPS, it is possible to analyze a large volume of data aggregated from various sensors. Accordingly, a variety of location-based services (LBSs) have attracted attention. To effectively provide these services, techniques for efficient spatial query processing have been studied. In this paper, we propose a method to overcome the limitation of not returning the desired query result to the user, because existing studies did not consider movement of the user. Specifically, we propose an algorithm to efficiently process a predictive query in the road network that returns the best available K moving objects, in consideration of the time of the user`s moving and that of the user`s waiting. In this process, we apply the technique to gradually expand the range of user and object`s movement simultaneously. Also, an appropriate index structure is used to efficiently process queries even in the road network with a large number of vertices and moving objects. Experimental results reveal the difference in the query result compared to existing studies and also reveal significant results in terms of efficiency.
Privacy Budget Allocation Technique Based on Variable Length Window for Traffic Data Publishing with Differential Privacy in Road Networks
Gunhyung Jo, Kangsoo Jung, Seog Park
http://doi.org/10.5626/JOK.2018.45.9.957
Recently, traffic volume data at every timestamp have been required in many fields such as road design and traffic analysis. Such traffic volume data may contain individual sensitive location information, which leads to privacy violation such as personal route exposure. Differential privacy has the advantage of protecting sensitive personal information in this situation while controlling the data utility by inserting noise to raw data. However, because of the traffic volume data generally would be an infinite size over time, there is a drawback in that data is useless because insufficiently large scaled noise is inserted. In order to overcome this drawback, researches have been conducted on applying the differential privacy technique only to the traffic volume data contained in windows of a certain time range. However, in the previous studies, the length of the window was fixed, inducing a limit whereby the correlation of the road sections and the time-specificity are not considered. In this paper, we propose a variable length window technique considering the correlation between road segments and time-specificity.
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