Semantic Web Service Clustering for Efficient Discovery Using an Ant-Based Method

Pop, Cristina Bianca; Chifu, Viorica Rozina; Salomie, Ioan; Dînşoreanu, Mihaela; David, Tudor; Acretoaie, Vlad

doi:10.1007/978-3-642-15211-5_3

Cited by 18 publications

(13 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…[19] clusters non-semantic services and extracts service abstractions as cluster representatives. [22] presents an ant-inspired method for clustering semantic services. [21] proposes clustering for classifying semantic services.…”

Section: A Service Discoverymentioning

confidence: 99%

“…Organization Description process fashion scheme [2] WSDL traditional top-down classes [12] OWL-S P2P & [13] WSDL-S UDDI [14] [15] P2P & DB [17] OWL-S abstractions [16] WSDL [18] WSDL traditional clusters [19] abstractions [20] clusters [21] OWL-S bottom-up [22] (off-line) [23] REST [24] WSDL Ours pragmatics self-adaptive on-line abstractions (b) Pragmatics-aware service discovery.…”

Section: Servicementioning

confidence: 99%

See 1 more Smart Citation

Self-Adaptive Service Organization for Pragmatics-Aware Service Discovery

Athanasopoulos

2017

2017 IEEE International Conference on Services Computing (SCC)

View full text Add to dashboard Cite

Traditional service registries form groups of functionally related services based on service descriptions, without taking into account past service-usage from the consumers' perspective, a.k.a. pragmatics. Thus, we propose a self-adaptive service-organization mechanism that follows an iterative and evolutionary life-cycle for autonomically evolving service groups by the arrival of pragmatics. Since pragmatics are not available beforehand, we propose an on-line serviceorganization algorithm that leverages the highly accumulated number of pragmatics. We evaluate the efficiency and effectiveness of our mechanism on the services of a publicly available benchmark and the results show that the effectiveness of the traditional service-organization is improved, while a low number of pragmatics is greedily stored.

show abstract

Section: A Service Discoverymentioning

confidence: 99%

Section: Servicementioning

confidence: 99%

Self-Adaptive Service Organization for Pragmatics-Aware Service Discovery

Athanasopoulos

2017

2017 IEEE International Conference on Services Computing (SCC)

View full text Add to dashboard Cite

show abstract

“…[5] and Ref. [33] present another semantic web services clustering approach using ant ontology concept. The semantic similarity is calculated using a matching method and a metrics set.…”

Section: B Functional-based Web Services Clusteringmentioning

confidence: 99%

“…As apparent in literature, there are numerous researches that propose validation indices to evaluate the number of clusters [69]. [33] No No Li (2010) [34] No No Wang et al (2009) [35] No No Nayak and Lee (2007) [36] No No [ 37,38] [47] No No Wong (2008, 2009) [ 48,49] The optimal clustering is found when it produces the high degrees of compactness and separation [70], [71]. The compactness is measured based on the closeness among cluster members.…”

Section: B Clustering Validationmentioning

confidence: 99%

Fuzzy-based Clustering of Web Services’ Quality of Service: A Review

Hasan¹,

Jaafar²,

Hassan³

2014

JCM

View full text Add to dashboard Cite

Clustering has been proposed in numerous researches on web services due to its ability in enhancing computational efficiency. The objective of this paper is to present a review on web services clustering and to propose fuzzy clustering of web services' based on quality of service (QoS). A total of 49 manuscripts, collected from ISI, SCOPUS and Google Scholar indexing databases, were critically reviewed. The review revealed that there is no report has been published before regarding fuzzy-based clustering of web services' QoS. It also revealed the importance of clustering in ensuring the efficiency of web services delivery particularly in their discovery, selection and recommendation processes. The results of the proposed fuzzy clustering can be used as reference to guide requestors to subscribe suitable services. They could also be implemented as the inference components for fuzzybased web services applications. For future work, the generated clustering results will be implemented in web services QoS monitoring application. Index Terms-Clustering, fuzzy clustering, Fuzzy C-Means, QoS clustering, web services clustering.

show abstract

“…However, the systematisation of the services is usually preliminary and artificial (Wang, Zhang, et al 2011). A few approaches (Pop et al 2010;Zhou et al 2013;Elgazzar, Hassan, and Martin 2010;Wu et al 2014;Zhang et al 2012) have been proposed to automatically cluster the services into domains. These methodologies cluster the services into domains based on the Web services description language (WSDL) or the quality of services (QoS).…”

Section: Introductionmentioning

confidence: 99%

Domain-aware reputable service recommendation in heterogeneous manufacturing service ecosystem

Fan

Huang

Tan

et al. 2014

International Journal of Computer Integrated Manufacturing

View full text Add to dashboard Cite

Networked manufacturing becomes an important manufacturing method for modern manufacturing enterprises. With the wide adoption of service-oriented architecture and cloud manufacturing, manufacturing enterprises and organisations publish their manufacturing capability, such as resources, processes and knowledge as manufacturing services. A rapidly growing manufacturing service ecosystem can be observed nowadays, which brings the information overload problem for the service selection. Thus, how to organise these services and how to recommend the reputable services become two important issues for the manufacturing service retrieval and reuse. In this paper, the service cluster method WSTPCluster, which develops the topic model based on Latent Dirichlet Allocation, is employed to cluster services into specific domains. As the service ecosystem can be modelled as a heterogeneous service network, a unified reputation propagation method URPM is proposed to calculate the reputation of services and to distinguish the reputable services in each domain. Combining WSTPCluster and URPM, domain-aware reputable service recommendation method is introduced to recommend the high reputable services in each domain for the consumers. Experiments show that this method brings a better performance both in the recommendation accuracy and in the long tail recommendation.

show abstract

Semantic Web Service Clustering for Efficient Discovery Using an Ant-Based Method

Cited by 18 publications

References 10 publications

Self-Adaptive Service Organization for Pragmatics-Aware Service Discovery

Self-Adaptive Service Organization for Pragmatics-Aware Service Discovery

Fuzzy-based Clustering of Web Services’ Quality of Service: A Review

Domain-aware reputable service recommendation in heterogeneous manufacturing service ecosystem

Contact Info

Product

Resources

About