By Guojun Wang, Yanbo Han, Gregorio Martínez Pérez

This ebook constitutes the refereed court cases 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 publication have been rigorously reviewed and chosen from 107 submissions. The papers hide a variety of themes within the fields of cloud/utility/Web computing/big facts; foundations of providers computing; social/peer-to-peer/mobile/ubiquitous/pervasive computing; service-centric computing versions; integration of telecommunication SOA and net providers; company technique integration and administration; and protection 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

Finally, we use the K-Medoids algorithm [5] to cluster Mashups into similar groups in functionality. Specially, in order to measure the similarity relations between Mashup clusters, each Mashup cluster is also represented by a TF-IDF vector, which is computed by simply aggregating the description of each Mashup in it. K-Medoids is a classical partitioning technique of clustering that clusters the data set of n objects into k clusters known a prior. e. it is a most centrally located point in the cluster.

A Novel Multi-granularity Service Composition Model 45 The test used simulated data sets SD (Simulated Data). Simulated dataset SD was mainly generated by imitating the real Web service features, including service id, service input, service output, type and QoS. The description of services inputs and outputs were used of the semantic description in semantic tree from China HowNet [17] randomly. QoS value was assigned by the random function of JAVA. The size of SD was controlled by the size parameters of JAVA algorithm artificially.

1. With these settings, our approach can perform better than other settings. e. GMrank [13], which used manifold learning as well. 3, so that GMrank can get the best performance on our dataset. e. MRrank) which is just like DMRrank but simply selects the most similar Mashup cluster according to the user’s requirement. Namely, DMRrank chooses one more similar Mashup cluster than MRrank and other settings are just keeping the same. Figures 4, 5 and 6 present the precision, recall and F-measure comparisons on the training data with different density.

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