Read e-book online Advances in Services Computing: 10th Asia-Pacific Services PDF
By Guojun Wang, Yanbo Han, Gregorio Martínez Pérez
This publication 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 ebook have been rigorously reviewed and chosen from 107 submissions. The papers conceal quite a lot of issues within the fields of cloud/utility/Web computing/big info; foundations of prone computing; social/peer-to-peer/mobile/ubiquitous/pervasive computing; service-centric computing types; integration of telecommunication SOA and net prone; enterprise approach 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 resources for Advances in Services Computing: 10th Asia-Pacific Services Computing Conference, APSCC 2016, Zhangjiajie, China, November 16-18, 2016, Proceedings
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.
Xn g & Rm and build a graph on the data. Let W 2 Rn Â n be the adjacency matrix wherein each entry wij represents the weight of the edge between point i and j. Let F : X ! R be a ranking function which assigns to each point xi via a ranking score Fi . Finally, we deﬁne an initial vector q ¼ ½q1 ; . ; qn T , in which qi ¼ 1 if xi is a query and qi ¼ 0 otherwise. The cost function associated with F is deﬁned to be 0 12 2 n X 1 1 1 OðFÞ ¼ @ wij pﬃﬃﬃﬃﬃﬃ Fi À pﬃﬃﬃﬃﬃﬃ Fj þ m kF i À qi kA 2 i; j¼1 Dii Djj i¼1 n X ð7Þ where Pv > 0 is the regularization parameter and D is a diagonal matrix with Dii ¼ nj¼1 wij .
The offline part includes the following main steps: Multi-relation Based Manifold Ranking Algorithm for API Recommendation 17 Fig. 1. The API recommendation framework of our approach • Cluster Mashups into groups so that Mashups within a cluster are closely connected based on the similarities of their textual descriptions. • Measure the similarity relations between Mashup clusters by using TF-IDF vectors generated from their textual descriptions. • Measure the similarity relation, composition relation, and potential composition relation between APIs.
Advances in Services Computing: 10th Asia-Pacific Services Computing Conference, APSCC 2016, Zhangjiajie, China, November 16-18, 2016, Proceedings by Guojun Wang, Yanbo Han, Gregorio Martínez Pérez