Fast Social Service Network Construction using Map-Reduce for Efficient Service Discovery

Service discovery and composition are challenging issue of service computing to provide value-added service. Existing approaches by keyword or ontology matching have limitations for locating realistic services discovery and composition considering non-functionality or sociality. On main reason in that approaches are based on isolated services. The isolation hinders efficient discovery and composition of services. Therefore, in the past research, they suggest social linked service network considering relationships of functional and nonfunctional properties, and social interaction based on complex network theory, where they can locate related services through sociability. However, it would be difficult to create social linked service network because services portable devices and sensors has been increasing with progress of Big Data technology. In this paper, we propose creating social linked service network to improve performance of network construction as considering distributed process on Big Data infrastructure. First, we propose an algorithm that creation network graph using Map Reduce parallel programming model. Finally, experimental results show that our creating network using Map Reduce approach can solve the heavy computation load for many calculations of network elements.