Skyline processing, an established preference evaluation technique, aims at discovering the best objects, i.e. those that are not dominated by any other objects, in satisfying the user's preferences. Most of the skyline approaches are limited to a single user query. However, in real world scenario, due to the advancement of technology, adhoc meetings are becoming more and more common. Thus, it is necessary to consider the preferences of a group of users, when they intend to meet while they are on the move. While there are studies that consider the preferences of a group of users, the skyline objects derived by their solutions are noncontinuous as they did not take into consideration the movements of the users, i.e. the current locations. Therefore, in this study we present our proposed framework which aims at deriving skylines for a group of users while they are moving (i.e. mobile users) towards an undecided meeting Find more related documents in Scopus based on: MSSQ: Manhattan spatial skyline queries
Recently, with the advancement of technology, ad-hoc meetings or impromptu gathering are becoming more and more common. The meetings/gatherings which involve at least two people will require a specific physical point location that is useful or interesting to them, called point of interest (PoI). These people might be residing at different locations; each with their own preferences which most likely to be different. Undoubtedly, given đť‘› people in a group, there will be đť‘› users' preferences. Finding a suitable PoI that meets these đť‘› users' preferences is not a straightforward task. Existing solutions that utilise skyline processing in discovering the best, most preferred objects in satisfying the preferences of a group of users within a predetermined area have shown acceptable results. However, these solutions have to be executed repeatedly for each group of user's queries since they do not exploit the possibilities that an area that has been visited by a group of users might be the area of interest of another group of users in the future. Inherently, they require rescanning the objects and recomputing the skylines of a previously visited region which is undoubtedly unwise and costly. This paper proposes the Region-based Skyline for a Group of Users (RSGU) and Extended Region-based Skyline for a Group of Users (ERSGU) frameworks which attempt to resolve the limitations of existing solutions. In this work, skylines objects are point of interests (PoIs) that are recommended to a group of users that are derived by analysing both the locations of the users, i.e. spatial attributes, as well as the spatial and non-spatial attributes of objects that are within a predetermined region of the group of users. Here, each region is partitioned into smaller units called fragments in such a way that overlapping areas between the currently and previously visited regions can be easily determined; while the results of computing the skylines of each fragment, known as fragment skylines, are saved to be utilised by the subsequent requests. Meanwhile, ERSGU has an additional feature in which the skylines derived for a group of users are not only based on the evaluation of the spatial and non-spatial attributes of the objects, but also the closeness of the objects to the desirable facilities or other interesting objects in the region. Undeniably, a PoI that is nearby to other attractions is appealing and worth the journey. Several experiments have been conducted and the results show that our proposed frameworks outperform the previous work with respect to CPU time.INDEX TERMS Multi-criteria decision making; skyline queries; group of users; spatial and non-spatial attributes.
Skyline processing, an established preference evaluation technique, aims at discovering the best, most preferred objects, i.e. those that are not dominated by other objects, in satisfying the user’s preferences. In today’s society, due to the advancement of technology, ad-hoc meetings or impromptu gathering are becoming more and more common. Deciding on a suitable meeting point (object)for a group of people (users) to meet is not a straightforward task especially when these users are located at different places with distinct preferences. A place which is close by to the users might not provide the facilities/services that meet all the users’ preferences; while a place having the facilities/services that meet most of the users’ preferences might be too distant from these users. Although the skyline operator can be utilised to filter the dominated objects among the objects that fall in the region of interest of these users, computing the skylines for various groups of users in similar region would mean rescanning the objects of the region and repeating the process of pair wise comparisons among the objects which are undoubtedly unwise. On this account, this study presents a region-based skyline computation framework which attempts to resolve the above issues by fragmenting the search region of a group of users and utilising the past computed skyline results of the fragments. The skylines, which are the objects recommended to be visited by a group of users, are derived by analysing both the locations of the users, i.e. spatial attributes, as well as the spatial and non-spatial attributes of the objects. Several experiments have been conducted and the results show that our proposed framework outperforms the previous works with respect to CPU time.
Skyline queries, which are based on the concept of Pareto dominance, filter the objects from a potentially large multi-dimensional collection of objects by keeping the best, most favoured objects in satisfying the user’s preferences. With today’s advancement of technology, ad hoc meetings or impromptu gatherings involving a group of people are becoming more and more common. Intuitively, deciding on an optimal meeting point is not a straightforward task especially when conflicting criteria are involved and the number of criteria to be considered is vast. Moreover, a point that is near to a user might not meet all the various users’ preferences, while a point that meets most of the users’ preferences might be located far away from these users. The task becomes more complicated when these users are on the move. In this paper, we present the Region-based Skyline for a Group of Mobile Users (RSGMU) method, which aims to resolve the problem of continuously finding the optimal meeting points, herein called skyline objects, for a group of users while they are on the move. RSGMU assumes a centroid-based movement where users are assumed to be moving towards a centroid that is identified based on the current locations of each user in the group. Meanwhile, to limit the searching space in identifying the objects of interest, a search region is constructed. However, the changes in the users’ locations caused the search region of the group to be reconstructed. Unlike the existing methods that require users to frequently report their latest locations, RSGMU utilises a dynamic motion formula, which abides to the laws of classical physics that are fundamentally symmetrical with respect to time, in order to predict the locations of the users at a specified time interval. As a result, the skyline objects are continuously updated, and the ideal meeting points can be decided upon ahead of time. Hence, the users’ locations as well as the spatial and non-spatial attributes of the objects are used as the skyline evaluation criteria. Meanwhile, to avoid re-computation of skylines at each time interval, the objects of interest within a Single Minimum Bounding Rectangle that is formed based on the current search region are organized in a Kd-tree data structure. Several experiments have been conducted and the results show that our proposed method outperforms the previous work with respect to CPU time.
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