A Framework for Processing Skyline Queries for a Group of Mobile Users

Dehaki, Ghoncheh Babanejad; Ibrahim, Hamidah; Udzir, Nur Izura; Sidi, Fatimah; Alwan, Ali A.

doi:10.1145/3282373.3282392

Cited by 3 publications

(7 citation statements)

References 8 publications

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“…Both the spatial and non-spatial attributes of the objects are analysed to find the skylines. Meanwhile, our previous solution, SGMU [9], is designed with the main aim to continuously derive skylines for a group of mobile users. In SGMU, while the users decide on a place to visit, the skylines are continuously updated since a place that was initially in the top list based on the users' locations at time might no longer be the place of interest at time where < since the users' locations at time might be different at time .…”

Section: Related Workmentioning

confidence: 99%

“…Although [9,19,26,27] considered the spatial attributes of the group of users in determining the skylines, but there is no attempt made to avoid rescanning of objects of previously visited regions and simultaneously avoid repeating the process of pairwise comparisons among the objects.…”

Section: Related Workmentioning

confidence: 99%

“…The density in synthetic dataset and real dataset of TIGER database is based on the number of point's falls into one square unit in normal. We ran our proposed framework, RSGU, SGMU [9], and VR algorithm [19] using randomly selected user points and objects in each dataset. Each dataset contains a spatial attribute and two non-spatial attributes.…”

Section: Performance Evaluationmentioning

confidence: 99%

“…Thus, it is essential to have a method that could find an object(s) within a predetermined region that dominates other objects with respect to both the spatial (location) and non-spatial attributes of the objects that best suits the preferences of a group of users. Although this has been well tackled by [9,19,26,27], there is no attempt made to resolve the following issue. Obviously, similar regions may have to be explored again in computing skylines for different groups of users (or even the same group of users at a different day/time).…”

Section: Introductionmentioning

confidence: 99%

“…repeating the process of pairwise comparisons among objects) are undoubtedly unwise and costly. Motivated by the above example, we propose a region-based skyline computation framework, RSGU, an enhancement of our previous framework, SGMU [9], with the main aim at avoiding the process of rescanning the objects of a previously visited region by utilising a fragmentation strategy as well as re computing the skylines for a group of users by utilising the past computed skyline results of the fragments. The skylines, which are the objects recommended to be visited by the group of users, are derived by analysing both the locations of these users, i.e.…”

Section: Introductionmentioning

confidence: 99%

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A Fragmentation Region-based Skyline Computation Framework for a Group of Users

Dehaki¹,

Ibrahim²,

Udzir³

et al. 2021

AI, Machine Learning and Applications

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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.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Performance Evaluationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A Fragmentation Region-based Skyline Computation Framework for a Group of Users

Dehaki¹,

Ibrahim²,

Udzir³

et al. 2021

AI, Machine Learning and Applications

Self Cite

View full text Add to dashboard Cite

show abstract

Efficient Region-Based Skyline Computation for a Group of Users

et al. 2022

Self Cite

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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.

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A Continuous Region-Based Skyline Computation for a Group of Mobile Users

et al. 2022

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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.

show abstract

A Framework for Processing Skyline Queries for a Group of Mobile Users

Cited by 3 publications

References 8 publications

A Fragmentation Region-based Skyline Computation Framework for a Group of Users

A Fragmentation Region-based Skyline Computation Framework for a Group of Users

Efficient Region-Based Skyline Computation for a Group of Users

A Continuous Region-Based Skyline Computation for a Group of Mobile Users

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