Advances in Services Computing: 10th Asia-Pacific Services by Guojun Wang, Yanbo Han, Gregorio Martínez Pérez

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By Guojun Wang, Yanbo Han, Gregorio Martínez Pérez

This ebook constitutes the refereed lawsuits 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 booklet have been conscientiously reviewed and chosen from 107 submissions. The papers disguise quite a lot of issues within the fields of cloud/utility/Web computing/big information; foundations of prone computing; social/peer-to-peer/mobile/ubiquitous/pervasive computing; service-centric computing types; integration of telecommunication SOA and net providers; company procedure integration and administration; and defense in services.

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

Example text

After obtained the above five matrices, namely, M, Asim , Acom , Apcom , P, we employ our multi-relation based manifold ranking algorithm to recommend APIs. e. the similarity relation between Mashup clusters and between APIs, the composition and potential composition relations between APIs, and the inclusion relation between Mashup clusters and APIs) into the original manifold ranking algorithm, our multi-relation based manifold ranking algorithm consists of seven terms, the first five terms are smoothness constraints, and the last two terms are fitting constraints.

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. • Measure the inclusion relation between Mashup clusters and APIs according to the number of times that an API included in a Mashup cluster.

In: Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 538–543. ACM (2002) 16. : Divrank: the interplay of prestige and diversity in information networks. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1009–1018. ACM (2010) 17. : Diversified ranking on large graphs: an optimization viewpoint. In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp.

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