By Guojun Wang, Yanbo Han, Gregorio Martínez Pérez

This ebook constitutes the refereed complaints of the tenth Asia-Pacific companies Computing convention, APSCC 2016, held in Zhangjiajie, China, in November 2016.

The 38 revised complete papers offered during this booklet have been conscientiously reviewed and chosen from 107 submissions. The papers disguise quite a lot of themes within the fields of cloud/utility/Web computing/big info; foundations of providers computing; social/peer-to-peer/mobile/ubiquitous/pervasive computing; service-centric computing types; integration of telecommunication SOA and internet prone; company approach integration and administration; and protection in services.

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Extra info for Advances in Services Computing: 10th Asia-Pacific Services Computing Conference, APSCC 2016, Zhangjiajie, China, November 16-18, 2016, Proceedings

Example text

Sgn g; sgj and sgk ; sg1 [ sg2 [ . . [ sgn À! sgj , if sg1 ; Rh sg2 ; . .. ; sgn ; sgj À! sgk ; the degree of polymerization between service grain sgk and SG, sgj can be expressed as: À Á AveAGD sg1 ; sg2 ; . .. ; sgn ; sgj ; sgk n P similar ðsgk :Input; sgi :InputÞ À Á þ w2 Â Similar sgk :Output; sgj :Output ¼ w1 Â i¼1 n ð8Þ Based on the degree of polymerization calculated by the formula (7) and (8), a coarse service grain can be found which is functionally equivalent to two or a set of service grains, and build a hierarchical relationship between them.

Almulla et al. [14] presented a new Web services selection model based on fuzzy logic and proposed a new fuzzy ranking algorithm based on the dependencies between proposed qualities attributes. Jeh and Widom [15] designed a general similarity measure called SimRank, which is based on a simple and intuitive graph-theoretic model and defines the similarity between two vertices in a graph by their neighbourhood similarity. Mei et al. [16] proposed a ranking approach called DivRank, which is based on a reinforced random walk in an information network.

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.

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