Efficient Computation of G-Skyline Groups

Wang, Changping; Guo, Gaoyang; Yao, Xiaojun; Yu, Philip S.

doi:10.1109/tkde.2017.2777994

Cited by 24 publications

(14 citation statements)

References 35 publications

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“…The common idea of the above algorithms is to generate a set enumeration tree including candidate point groups, and at the same time, prune the non-group skyline to enumerate the candidate group skyline. Further, the efficiency of g-skyline computation was investigated and advanced in [16]- [18]. In addition, the group skyline model has been applied over multivalued attributed graphs [10], [19].…”

Section: A Group Skyline Queriesmentioning

confidence: 99%

Skyline (λ, k)-Cliques Identification From Fuzzy Attributed Social Networks

Hao

Gao

Chen

et al. 2022

IEEE Trans. Comput. Soc. Syst.

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Identifying the optimal groups of users that are closely connected and satisfy some ranking criteria from an attributed social network, attracts significant attention from both academia and industry. Skyline query processing, a multi-criteria decision-making optimized technique, is recently embedded into cohesive subgraphs mining in graphs/social networks. However, the existing studies cannot capture the fuzzy property of connections between users in social networks. To fill this gap, in this paper we formulate a novel model of the skyline (λ, k)cliques over a fuzzy attributed social network and develop a Formal Concept Analysis (FCA) based skyline (λ, k)-cliques identification algorithm. Specifically, λ can be regarded as a quality control parameter for measuring the stability of the cohesive groups. Extensive experimental results conducted on three real-world datasets demonstrate the effectiveness of the skyline (λ, k)-clique model in a fuzzy attributed social network. Further, an illustrative example is executed for revealing the usefulness of our model. It is expected that our proposed skyline (λ, k)-clique model can be widely used in various graph-based computational social systems, such as optimal team formation in crowdsourcing, and group recommendation in social networks.

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Section: A Group Skyline Queriesmentioning

confidence: 99%

Skyline (λ, k)-Cliques Identification From Fuzzy Attributed Social Networks

Hao

Gao

Chen

et al. 2022

IEEE Trans. Comput. Soc. Syst.

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“…Given two points x and y, x dominates y means that the values of x are not worse than those of y in any dimension, and better at least in one dimension. In general, the skyline of a data point set X is the set of best points in X, and different skyline points represent various trade-offs between the dimensions [26][27][28][29][30].…”

Section: Related Workmentioning

confidence: 99%

The Skyline Operator for Selection of Virtual Machines in Mobile Computing

Alguliyev¹,

Alıguliyev²,

Alakbarov³

et al. 2018

IJMECS

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The article presents an algorithm to solve a problem of placing mobile users' queries (tasks or software applications) on balanced virtual machines (VMs) on cloudlets located near base stations of Wireless Metropolitan Area Networks (WMAN) taking into account their technical capabilities. For this purpose, hierarchically structured network architecture and the algorithm for selection of VMs on cloudlets satisfying the user's requirements (execution time and cost) are proposed. For selection of an optimal VM from the set of VMs the algorithm, based on Skyline operator is proposed.

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“…(2) Aiming at the ranking strategy of skyline layer, the concept of vertex coverage is proposed to deal with the situation that the ranking of result point groups is the same. [21][22][23][24]. In recent years, effective query results [25][26][27]get more attention.…”

Section: Introductionmentioning

confidence: 99%

“…In recent years, effective query results [25][26][27]get more attention. For reducing the size of the query result set and returning more representative Skyline points, the variations of Skyline definitions such as k -dominated Skyline [24], representative Skyline [28], top-k Skyline query, and distance-based representative Skyline [29] are given. Basic algorithms of group Skyline query include algorithm PointWise [12], UnitWise [12], and Unit-Wise+ [12].…”

Section: Introductionmentioning

confidence: 99%

Optimized Query Algorithms for Top- $K$ Group Skyline

Liu

Chen

et al. 2022

Wireless Communications and Mobile Computing

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Skyline query is a typical multiobjective query and optimization problem, which aims to find out the information that all users may be interested in a multidimensional data set. Multiobjective optimization has been applied in many scientific fields, including engineering, economy, and logistics. It is necessary to make the optimal decision when two or more conflicting objectives are weighed. For example, maximize the service area without changing the number of express points, and in the existing business district distribution, find out the area or target point set whose target attribute is most in line with the user’s interest. Group Skyline is a further extension of the traditional definition of Skyline. It considers not only a single point but a group of points composed of multiple points. These point groups should not be dominated by other point groups. For example, in the previous example of business district selection, a single target point in line with the user’s interest is not the focus of the research, but the overall optimality of all points in the whole target area is the final result that the user wants. This paper focuses on how to efficiently solve top- k group Skyline query problem. Firstly, based on the characteristics that the low levels of Skyline dominate the high level points, a group Skyline ranking strategy and the corresponding SLGS algorithm on Skyline layer are proposed according to the number of Skyline layer and vertices in the layer. Secondly, a group Skyline ranking strategy based on vertex coverage is proposed, and corresponding VCGS algorithm and optimized algorithm VCGS+ are proposed. Finally, experiments verify the effectiveness of this method from two aspects: query response time and the quality of returned results.

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Efficient Computation of G-Skyline Groups

Cited by 24 publications

References 35 publications

Skyline (λ, k)-Cliques Identification From Fuzzy Attributed Social Networks

Skyline (λ, k)-Cliques Identification From Fuzzy Attributed Social Networks

The Skyline Operator for Selection of Virtual Machines in Mobile Computing

Optimized Query Algorithms for Top- $K$ Group Skyline

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Efficient Computation of G-Skyline Groups

Cited by 24 publications

References 35 publications

Skyline (λ, k)-Cliques Identification From Fuzzy Attributed Social Networks

Skyline (λ, k)-Cliques Identification From Fuzzy Attributed Social Networks

The Skyline Operator for Selection of Virtual Machines in Mobile Computing

Optimized Query Algorithms for Top- K Group Skyline

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Product

Resources

About

Optimized Query Algorithms for Top- $K$ Group Skyline