Web Services Clustering via Exploring Unified Content and Structural Semantic Representation

Kang, Guosheng; Liu, Jianxun; Xiao, Yong; Cao, Yingcheng; Cao, Buqing; Shi, Min

doi:10.1109/tnsm.2022.3197725

Cited by 17 publications

(13 citation statements)

References 47 publications

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“…In fact, the QoS-aware service composition has been widely explored for Web services or cloud services in the service computing field [9][10][11][12][13][14]. By reviewing the literature, the techniques for QoS-aware service composition can be classified into four categories: local maximization approaches, linear optimization approaches, approximation approaches, and Pareto-optimization approaches [15].…”

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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Section: Related Workmentioning

confidence: 99%

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

et al. 2023

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“…Service composition is widely studied in the traditional service computing field under Internet environment 17‐19 . By reviewing the existing works, they can be roughly divided into three categories: (1) independent service selection approaches ; 20‐22 (2) integer‐programming based approaches ; 23,24 and (3) meta‐heuristic based approaches 25‐28 …”

Section: Related Workmentioning

confidence: 99%

“…If S neibor is better than the current solution, then replace it. Thus, S teacher is updated with Equation (18).…”

Section: [ T Teacher C Teachermentioning

confidence: 99%

Spatial‐temporal aware service composition for production factors under industrial internet

Liu

Xie

Kang

et al. 2023

Concurrency and Computation

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Industrial Internet is a promising technology combining industrial systems with Internet techniques to significantly improve production efficiency and reduce cost by cooperating with intelligent devices. Under industrial internet environment, a production process usually consists of multiple subtasks, and one or more types of product factors are needed to finish a subtask. Thus, the service composition under industrial application is more complex and challenging compared with traditional service composition under the Internet environment. In this article, we model the problem of service composition for production factors under industrial internet as a multiobjective optimization problem. To derive the optimal Pareto service composition plans, we propose a hybrid optimization algorithm, named TLBO-TS, by combining the advantages of teaching-learning-based optimization algorithm and tabu search algorithm.Extensive experiments are conducted to compare with other population-based optimization methods under a real-world ship production process to verify the superiority of our approach.

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“…Thus, QoS-aware service recommendation becomes an effective way to alleviate the problem. 5 There are many QoS-aware service recommendation methods including utility-based methods, 6,7 Skyline-based methods, [8][9][10] collaborative filtering-based methods, [11][12][13][14] matrix factorization-based methods, 15,16 and factorization machine-based methods. [17][18][19] The utility-based and Skyline-based methods assume that users can provide numerical QoS preferences or weights, which is difficult to hold in the real world.…”

Section: Introductionmentioning

confidence: 99%

“…It is known that QoS‐aware service composition optimization is an NP‐hard problem, 4 which makes the problem unsolvable in the scenario of a large number of functionally equivalent Web services. Thus, QoS‐aware service recommendation becomes an effective way to alleviate the problem 5 …”

Section: Introductionmentioning

confidence: 99%

QoS‐aware web service recommendation via exploring the users' personalized diversity preferences

Kang

Liang

Ding

et al. 2023

Engineering Reports

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With the popularity and wide adoption of SOA (service‐oriented architecture), a massive amount of Web services emerge on the Internet. It is difficult for users to find the desired services from a large number of services. Thus, service recommendation becomes an effective means to improve the efficiency of using service. Considering that the users' QoS (quality of service) preferences are often unknown or uncertain, the recent QoS‐aware service recommendation methods recommend QoS‐diversified services for users to increase the probability of fulfillment of the service list with a limited number of services on users' potential QoS preferences. However, the existing QoS‐diversified service recommendation methods recommend services with a uniform diversity degree for different users, while the diversified preference requirements are not considered. To this end, this article proposes a service diversity adjustment algorithm, which selects more diversified services outside of the original service recommendation list to replace the services in the present recommendation list to approximate the QoS diversity preference of the active user. In this way, the probability of meeting the user's potential QoS preference requirements is improved. Comprehensive experimental results show that the proposed approach can not only provide personalized and diversified services but also ensure the overall accuracy of the recommendation results.

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Web Services Clustering via Exploring Unified Content and Structural Semantic Representation

Cited by 17 publications

References 47 publications

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

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

Spatial‐temporal aware service composition for production factors under industrial internet

QoS‐aware web service recommendation via exploring the users' personalized diversity preferences

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