Utilizing Web Services Networks for Web Service Innovation

Mokarizadeh, Shahab; Küngas, Peep; Matskin, Mihhail

doi:10.1109/icws.2014.95

Cited by 7 publications

(3 citation statements)

References 27 publications

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“…Combination between Web Service services, service is also a hot topic. [11,12,13] Maolin Tang and Lifeng Ai proposed a genetic algorithm based on constraints of best choice service composition method, good results have been achieved in the evaluation experiment. Chao Ma and Yanxiang He put forward a kind of service composition visualization and formalized method, greatly simplifies the operational ease of service composition.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

A Service Pipelining Model for Web GIS Based on Distributed Environment

Yu¹,

Cheng²

2012

IJGDC

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Pipeline is one of the classic technologies to enhance efficiency. When the amount of data is becoming more and more big when processing, it becomes a big problem that how to improve the efficiency with existing resources. In the meantime, a large number of older hardware and software feel difficult to deal with new task in new environment with the accumulation of time. In order to ameliorate efficiency of web GIS with low cost, this paper propose a service pipelining model for Web GIS based on distributed environment. The system build a basic service library to reuse plenty of existing resource, and design a segment system to organize pipeline in level of service to improve efficiency. The model can be used in many platforms and occasions, not limited to the application we proposed in this paper. The model we proposed has been proved work well in our experiments and test.

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Section: Related Workmentioning

confidence: 99%

“…NET platform, Java, XML specification has been very mature. [7,8] Web Service platform system XSD data types and object information transmission protocol SOAP and Web services description language (WSDL), as well as the electronic commerce standard UDDI. [9,10]there are various forms of implementation and encapsulation.…”

Section: Related Workmentioning

confidence: 99%

A Service Pipelining Model for Web GIS Based on Distributed Environment

Yu¹,

Cheng²

2012

IJGDC

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“…Accompanied by the rising of deep learning technology, various deep web service quality prediction models have been continuously proposed [3][4][5]. Deep learning methods such as CNN and RNN can extract hidden information that cannot be treated by traditional matrix decomposition technology [6,7] to improve the accuracy, but these methods do not take the topology information into account at all.…”

Section: Introductionmentioning

confidence: 99%

Relationship Discovery and Hierarchical Embedding for Web Service Quality Prediction

Xia

et al. 2022

Computational Intelligence and Neuroscience

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Web Services Quality Prediction has become a popular research theme in Cloud Computing and the Internet of Things. Graph Convolutional Network (GCN)-based methods are more efficient by aggregating feature information from the local graph neighborhood. Despite the fact that these prior works have demonstrated better prediction performance, they are still challenged as follows: (1) first, the user-service bipartite graph is essentially a heterogeneous graph that contains four kinds of relationships. Previous GCN-based models have only focused on using some of these relationships. Therefore, how to fully mine and use the above relationships is critical to improving the prediction accuracy. (2) After the embedding is obtained from the GCNs, the commonly used similarity calculation methods for downstream prediction need to traverse the data one by one, which is time-consuming. To address these challenges, this work proposes a novel relationship discovery and hierarchical embedding method based on GCNs (named as RDHE), which designs a dual mechanism to represent services and users, respectively, designs a new community discovery method and a fast similarity calculation process, which can fully mine and utilize the relationships in the graph. The results of the experiment on the real data set show that this method greatly improved the accuracy of the web service quality prediction.

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