IQSrec: An Efficient and Diversified Skyline Services Recommendation on Incomplete QoS

Shu, Yanjun; Zhang, Jianhang; Zhang, Wei Emma; Zuo, Decheng; Sheng, Quan Z.

doi:10.1109/tsc.2022.3189503

Cited by 9 publications

(6 citation statements)

References 35 publications

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“…Faster skyline selection, reduced composition time, dataset sharing, and resources integration assure the QoS over multiple clouds. Shu et al [38] proposed a novel dimension-based partition on incomplete QoS, which can quickly divide all services into partitions based on the QoS probability distribution. The computation process of skyline probability is improved by reducing the number of skyline candidates and revising the computation algorithm, which greatly improves the computation efficiency of skyline probability.…”

Section: Service Skyline Computingmentioning

confidence: 99%

Uncertain Big QoS Data-Driven Efficient SaaS Decision-Making Method

Zhang,

Bai

2024

IEEE Access

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Selecting the QoS-optimized software-as-a-service (SaaS) from a large number of services with the same functionality and different Quality of Service (QoS) is still a hot issue. Massive QoS feedback forms big QoS data, which exhibits ambiguity and randomness increasing the uncertainty of service selection. Starting from the characterization of big QoS data, the Uncertain Big QoS data-driven Efficient SaaS Decision Making Method (UBQoS_ESDM) is proposed. The method firstly utilizes cloud model to portray QoS in order to solve the problem of inaccurate description of uncertain big QoS data; then draws on the idea of Skyline query to establish uncertain service Skyline set, which reduces the search space and improves the efficiency of QoS-optimal SaaS selection; and draws on the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) to design two decision-making algorithms to evaluate alternative SaaSs, and to obtain the QoS-optimal SaaS that reflects user requirements. In addition, two types of backward QoS cloud generators are introduced to convert big QoS data to QoS cloud models, and the QoS cloud model adaptive adjustment mechanism is introduced too, which can adapt to the dynamic changes of QoS. Finally, theoretical proofs and experiments verify the superiority and efficiency of the method.

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Section: Service Skyline Computingmentioning

confidence: 99%

Uncertain Big QoS Data-Driven Efficient SaaS Decision-Making Method

Zhang,

Bai

2024

IEEE Access

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“…Based on the above observations, different QoS properties should be normalized to the same range without explicit units. In this work, QoS normalization is achieved by the max-min normalization technique, as used in the previous literature [34]. To be more specific, the max-min normalization formulas are shown in Formula ( 1) and ( 2), and used for the normalization of positive and negative QoS properties, respectively.…”

Section: Qos Normalizationmentioning

confidence: 99%

“…Due to the large number of service candidates for each task, it would be more efficient for service selection if the number could be reduced while not comprising the quality. Consequently, we use the Skyline query technique from database theory to select the optimal services preliminarily [34], thus, the service number for service selection could be reduced greatly. Since the Skyline services are the ones that are not dominated by any other services from the QoS perspective, they are the better ones on QoS.…”

Section: Qos Properties/structures Response Time Cost Availabilitymentioning

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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“…Utility-based recommendation methods calculate the overall QoS utility based on the QoS properties [32,33]. The services with the highest QoS utility are recommended as the results to users.…”

Section: Related Workmentioning

confidence: 99%

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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IQSrec: An Efficient and Diversified Skyline Services Recommendation on Incomplete QoS

Cited by 9 publications

References 35 publications

Uncertain Big QoS Data-Driven Efficient SaaS Decision-Making Method

Uncertain Big QoS Data-Driven Efficient SaaS Decision-Making Method

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

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

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