By Guojun Wang, Yanbo Han, Gregorio Martínez Pérez
This ebook constitutes the refereed complaints of the tenth Asia-Pacific prone Computing convention, APSCC 2016, held in Zhangjiajie, China, in November 2016.
The 38 revised complete papers awarded during this e-book have been rigorously reviewed and chosen from 107 submissions. The papers conceal a variety of themes within the fields of cloud/utility/Web computing/big facts; foundations of prone computing; social/peer-to-peer/mobile/ubiquitous/pervasive computing; service-centric computing types; integration of telecommunication SOA and net companies; enterprise technique integration and administration; and safeguard in services.
Read Online or Download Advances in Services Computing: 10th Asia-Pacific Services Computing Conference, APSCC 2016, Zhangjiajie, China, November 16-18, 2016, Proceedings PDF
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Additional info for Advances in Services Computing: 10th Asia-Pacific Services Computing Conference, APSCC 2016, Zhangjiajie, China, November 16-18, 2016, Proceedings
Then, do service composition in SGS. This model can be expected not only to provide users with multi-granularity service compositions but also improve the efﬁciency of service composition. The structure of this paper is as following: Sect. 2 presents related work. Section 3 introduces our research. Section 4 details the SGS. Section 5 describes algorithm based on SGS. Section 6 evaluates the performance of the SGS algorithm. Section 7 concludes this work. A Novel Multi-granularity Service Composition Model 35 2 Related Work The way of organization and management of services have a signiﬁcant impact on the efﬁciency of the service composition, while service correlation mining is the premise of an effective organization for services.
GMrank , which used manifold learning as well. 3, so that GMrank can get the best performance on our dataset. e. MRrank) which is just like DMRrank but simply selects the most similar Mashup cluster according to the user’s requirement. Namely, DMRrank chooses one more similar Mashup cluster than MRrank and other settings are just keeping the same. Figures 4, 5 and 6 present the precision, recall and F-measure comparisons on the training data with different density. As we can see, with the increasing density of training data, the precision, recall and F-measure values also grow.
QoS value was assigned by the random function of JAVA. The size of SD was controlled by the size parameters of JAVA algorithm artiﬁcially. 1 Parameter The Optimal Granular. To reflect how the granular have an impact on the performance of service composition, a size of 1000 services are simulated. Change the granular and number of services grain by changing the similarity parameter between the services. Get ﬁve service compositions for each degree of similarity. Use the average response time and the average number of granular as the ﬁnal result.