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 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 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.
Deploying a solution for handling critical decision-based problem efficiently requires the processing of high-dimensional data. Over the years, due to modern technological advancement, unprecedented volume of uncertain data is been captured and this has necessitated the need to organize such data for better data access performance. To this effect, the use of indexing technique for supporting, organizing, and storing of uncertain data with high dimensionality has become pertinent. However, the choice of an indexing technique to improve search performance is highly influenced by the properties of the underlying data set, data construction methods employed by the indexing structure, and the query types it supports. This paper is motivated to conduct an extensive performance analysis among existing indexing techniques, namely: R-tree, R*-tree and X -tree, in order to realize the most efficient indexing structure for organizing, storing and ultimately improving search performance over uncertain data with high dimensionality. The results of the analyses with regard to CPU processing time and number of nodes visited clearly show the superiority of X -tree over R-tree and R*-tree, as its superiority holds for different data set sizes, data distributions, number of dimensions and even with varying selectivity ratio.INDEX TERMS Data partitioning, indexing techniques, MBR, uncertain data, high-dimensional data.
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