Efficient Region-Based Skyline Computation for a Group of Users

• Published in: IEEE access 2022, Vol.10, p.94496-94517
• Main Authors: Dehaki, Ghoncheh Babanejad, Ibrahim, Hamidah, Alwan, Ali A., Sidi, Fatimah, Udzir, Nur Izura, Lawal, Ma'aruf Mohammed
• Format: Article
• Language: English
• Subjects: Decision making; Distributed databases; group of users; Meetings; Multi-criteria decision making; Object recognition; Query processing; skyline queries; spatial and non-spatial attributes; Spatial databases; User requirements
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2022.3204115

Recently, with the advancement of technology, ad-hoc meetings or impromptu gatherings 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 n people in a group, there will be n users' preferences. Finding a suitable PoI that meets these n 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 query of a group of users 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 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 ar