Efficient Region-Based Skyline Computation for a Group of Users

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 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.