By Guojun Wang, Yanbo Han, Gregorio Martínez Pérez
This ebook constitutes the refereed lawsuits of the tenth Asia-Pacific companies Computing convention, APSCC 2016, held in Zhangjiajie, China, in November 2016.
The 38 revised complete papers provided during this e-book have been conscientiously reviewed and chosen from 107 submissions. The papers conceal a variety of subject matters within the fields of cloud/utility/Web computing/big information; foundations of companies computing; social/peer-to-peer/mobile/ubiquitous/pervasive computing; service-centric computing types; integration of telecommunication SOA and internet companies; enterprise strategy integration and administration; and safeguard in services.
Read 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
Sgn ; sgj ¼ i¼1 n ð6Þ The identiﬁer “n” is the number of service grains. Based on the formula (5) and (6), a correlation matrix SGCM can be formed after service correlation mining. The value 1 and 0 of the matrix respectively indicated whether has a correlation or not. 3 Construction of Hierarchical Service Granular Space SGS After the service correlation mining, whether there exists a coarse service grain can be checked that can accomplish the functions which should be completed by several service grains.
The model ﬁrstly constructs service granularity by service clustering. And then constructs the service granularity space according to the relationships between service granularities. So the process of getting appropriate service compositions can be transformed into getting service compositions from different granularity layers. Through experimental analysis, we can demonstrate that this model can provide users with different granularity service compositions which meet the multiple granularity demands of users.
Herein, we use the three measures of precision, recall, F-measure to evaluate the performance of our approach. The evaluation metrics are deﬁned as follows. • Precision: p¼ TA \ EA jTA j ð19Þ where, TA is the API recommendation result list, EA is the testing Mashup actually used APIs, |TA| is the size of the API recommendation result list. 28 F. Xie et al. • Recall: r¼ TA \ EA jEA j ð20Þ where, |EA| is the number of APIs the testing Mashup actually used. • F-measure: F¼ 2ÃpÃr pþr ð21Þ It is a comprehensive evaluation on the precision and recall.