Abstraction Refinement Approach for Web Service Selection using Skyline Computations

Wu, Zhi‐Yong; Meng, Ke; Yan, Xiukun; Shi, Dayin; Hu, Benjia

doi:10.1109/services51467.2021.00038

Cited by 3 publications

(4 citation statements)

References 15 publications

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“…-DSL-RS : This baseline approach first chooses all non-dominated services as Skyline services, named S DSL , and then randomly selects k services from S DSL . -DSL-KNN [22]: This is the first approach to solve the personalized QoS-centric service recommendation problem. It models service recommendation as a k nearest neighbor problem.…”

Section: Methodsmentioning

confidence: 99%

“…Then, a novel and diverse service ranking algorithm is proposed to identify the top-k services. Further, considering that QoS properties may be correlated with each other, Zhang et al [22] propose an algorithm based on KNN and dynamic Skyline services to recommend services close to the user's QoS constraints. This method requires users to provide the probability of QoS constraints and domination, and then maps the candidate service set at each time point to the original service space according to its quality value.…”

Section: Related Workmentioning

confidence: 99%

“…To consider the characteristics of the user's QoS constraints, Zhang et al [22] propose the KNN-DSL and DSL-KNN approaches. However, these approaches neglect the QoS correlation between different QoS properties.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

QoS-Centric Diversified Web Service Recommendation Based on Personalized Determinantal Point Process

Kang

Liang

et al. 2023

Electronics

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With the popularity and widespread adoption of the SOA (Service-Oriented Architecture), the number of Web services has increased exponentially. Users tend to use online services for their daily business and software development needs. With the large number of Web service candidates, recommending desirable Web services that meet users’ personalized QoS (Quality of Service) requirements becomes a challenging research issue, as the QoS preference is usually difficult to satisfy for users, i.e., the QoS preference is uncertain. To solve this problem, some recent works have aimed to recommend QoS-diversified services to enhance the probability of fulfilling the user’s latent QoS preferences. However, the existing QoS-diversified service recommendation methods recommend services with a uniform diversity degree for different users, while the personalized diversity preference requirements are not considered. To this end, this paper proposes to mine a user’s diversity preference from the their service invocation history and provides a Web service recommendation algorithm, named PDPP (Personalized Determinantal Point Process), through which a personalized service recommendation list with preferred diversity is generated for the user. Comprehensive experimental results show that the proposed approach can provide personalized and diversified Web services while ensuring the overall accuracy of the recommendation results.

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Section: Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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QoS-Centric Diversified Web Service Recommendation Based on Personalized Determinantal Point Process

Kang

Liang

et al. 2023

Electronics

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“…This category tries to find multiple optimal solutions with respect to one or more objective functions based on the approximation approaches by applying the Skyline technique for candidate service selection and optimal solution selection, such as [6,25]. Besides, Wu et al [26] took the difference between non-functional attributes into account, and subsidiary functions between different candidate services as well. They propose to group candidate services with the same subsidiary function into the same category so that the number of candidates can be further reduced after Skyline computation.…”

Section: Related Workmentioning

confidence: 99%

Scalable and Optimal QoS-Aware Manufacturing Service Composition via Business Process Decomposition

et al. 2023

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With the adoption of service-oriented manufacturing modes, more and more manufacturing services are released over manufacturing service platforms. As it is known, the problem of the QoS (Quality of Service)-aware manufacturing service composition is NP-hard. Thus, the optimization remains a challenging research issue, especially in the situation of large-scale manufacturing service data which arouse a scalability problem as well. To improve both the optimization performance and scalability of the QoS-aware manufacturing service composition, this paper proposes a scalable and optimal QoS-aware manufacturing service composition approach via business process decomposition. Specifically, the service composition process is decomposed by using a refined process structure tree (RPST). Moreover, an optimized service composition is achieved layer by layer based on the refined process structure tree in a bottom-up manner. For the atomic tasks or the compound tasks in the same layer of RPST, the corresponding QoS-aware service selection is optimized by calculating Skyline services, which can be carried out in parallel if necessary. When the optimization arrives at the root node, the complete service composition plans are derived. In our approach, the optimal manufacturing service candidates are picked out stage by stage. In this way, both the optimality and scalability of the whole approach can be guaranteed. Extensive experiments are conducted to verify the optimality and scalability of our approach.

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Caching Hierarchical Skylines for Efficient Service Composition on Service Graphs

Alhosaini

Wang

Yao

et al. 2022

2022 IEEE International Conference on Services Computing (SCC)

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Abstraction Refinement Approach for Web Service Selection using Skyline Computations

Cited by 3 publications

References 15 publications

QoS-Centric Diversified Web Service Recommendation Based on Personalized Determinantal Point Process

QoS-Centric Diversified Web Service Recommendation Based on Personalized Determinantal Point Process

Scalable and Optimal QoS-Aware Manufacturing Service Composition via Business Process Decomposition

Caching Hierarchical Skylines for Efficient Service Composition on Service Graphs

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