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

By Guojun Wang, Yanbo Han, Gregorio Martínez Pérez

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

The 38 revised complete papers awarded during this ebook have been rigorously reviewed and chosen from 107 submissions. The papers disguise a variety of issues 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 internet prone; company method 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

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 Parameter The Optimal Granular. To reflect how the granular have an impact on the performance of service composition, a size of 1000 services are simulated. Change the granular and number of services grain by changing the similarity parameter between the services.

Granular computing, as one of the new concepts and methods in the field of artificial intelligence, is an effective tool to deal with uncertain problems. The feasibility of using granular computing theory to build the multi-granularity services space is as follows: Build the services which have same or similar functions into service grains. Build the multi-granularity service space by the relationships between services. The advantages are that service composition can be sought and transformed in different granular layers, which will not only meeting the complex user needs but also assist the user who lack expertise knowledge to service composition.

An example of service composition Granular computing originated from the 1970 is a simulation of solving problems in a human way. As Prof. Zhang Ling and Zhang Bo said: “The common characteristic of human intelligence is that individuals can observe and analyze the same problem in different granular. ” So the theory of granular computing is considered to achieve the goal of getting multi-granularity service composition. Granular computing, as one of the new concepts and methods in the field of artificial intelligence, is an effective tool to deal with uncertain problems.

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