Title
Efficient Service Discovery Using Social Service Network Based on Big Data Infrastructure
Abstract
Service discovery and composition are a challenging issue for service computing when providing a value-added service. Existing approaches by keyword or ontology matching have limitations for locating realistic service discovery and composition that consider non-functionality or sociality. The main reason is that approaches are based on isolated services. The isolation hinders efficient discovery and composition of services. Therefore, past research suggests a social linked service network that considers relationships of functional and nonfunctional properties, and social interaction based on complex network theory, where related services can be located through sociability. However, it is difficult to create a social linked service network because services, portable devices, and sensors have been increasing in number with the progress of Big Data technology. In this paper, we propose creating a social linked service network to improve the performance of network construction by considering the distributed process on Big Data infrastructure. First, we propose an algorithm that creates a network graph using a Map-Reduce parallel programming model. Second, we evaluate the performance of network graph generation and service discovery. The experimental results show that our network created by using the Map-Reduce approach can solve the heavy computation load for the many calculations of network elements. In addition, service discovery performance is very similar to that of a none-distributed model.
Year
DOI
Venue
2017
10.1109/MCSoC.2017.9
2017 IEEE 11th International Symposium on Embedded Multicore/Many-core Systems-on-Chip (MCSoC)
Keywords
Field
DocType
service discovery,social service network,map-reduce operation,scale-free network,Hadoop
Ontology (information science),Services computing,Computer science,Quality of service,Linked data,Complex network,Network element,Service discovery,Big data,Distributed computing
Conference
ISBN
Citations 
PageRank 
978-1-5386-3442-4
0
0.34
References 
Authors
8
3
Name
Order
Citations
PageRank
Incheon Paik124138.80
Yutaka Koshiba210.71
T. H. Akila S. Siriweera332.10