DocumentCode :
2858871
Title :
Composable correlation mining of cloud service in cloud manufacturing
Author :
Guo, Hua ; Zhang, Lin ; Tao, Fei ; Ren, Zhiyun ; Luo, Yongliang
Author_Institution :
Sch. of Autom. Sci. & Electr. Eng., Beihang Univ., Beijing, China
fYear :
2011
fDate :
6-9 Dec. 2011
Firstpage :
1907
Lastpage :
1911
Abstract :
The emergence of cloud manufacturing (CMfg) provides a new opportunity for the change of manufacturing towards service-oriented model. Cloud service composition (CSC), which can realize the added value of cloud service (CS), is the core to implement CMfg. Since there always exist correlations among CSs, especially composable correlation (CoC), which can affect the construction of CSC path. Hence, how to mine the CoC among CSs and judge which kind of CoC between them is a key issue. This paper presents the formalized description for CoC, and designs decision algorithms to judge CoCs between CSs based on bipartite graph. The case study illustrates the application of proposed algorithms.
Keywords :
cloud computing; graph theory; service-oriented architecture; bipartite graph; cloud manufacturing; cloud service composition; composable correlation mining; decision algorithm; service-oriented model; Bipartite graph; Cascading style sheets; Correlation; Manganese; Optimal matching; Silicon; Transforms; Composabale correlation; cloud manufacturing (CMfg); cloud service composition; mining algorithm;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Industrial Engineering and Engineering Management (IEEM), 2011 IEEE International Conference on
Conference_Location :
Singapore
ISSN :
2157-3611
Print_ISBN :
978-1-4577-0740-7
Electronic_ISBN :
2157-3611
Type :
conf
DOI :
10.1109/IEEM.2011.6118247
Filename :
6118247
Link To Document :
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