@article {378,
	title = {Exception detection for web service composition using improved bayesian network},
	journal = {Journal of Digital Information Management},
	volume = {11},
	year = {2013},
	note = {Export Date: 10 July 2014},
	month = {2013},
	pages = {108 - 114},
	abstract = {New application systems generated by composition of web services dynamically have become a development trend in network environment. However, since a variety of external services are invoked with different quality of service during processing, the problem of how to keep the execution stable must be addressed in order to improve the reliability and availability of the combination of services. In this paper, an approach of exception detection is presented for web service composition by improved Bayesian network. Firstly, the topological structure of Bayesian network is established, where the causal relationships between underlying web services are mapped to Bayesian network in service combination process. The conventional Bayesian network algorithm is improved to satisfy the demand of better conversion from nodes in web service composition to nodes in Bayesian network. Secondly, the parameter setting in Bayesian network is explored for determining the prior probability of service nodes and the conditional probability of service output node. Thirdly, the algorithm of the improved exception detection model is introduced, with the aim of locating the web services that cause exceptions in the execution of composite services. Lastly, a case study is given and the results analysis is also conducted. The results show that the presented approach not only considers the uncertainty during exception detection, but also can identify the exception web services in the process.},
	keywords = {Bayesian network, Exception handling, Web service, Web service composition},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84879099166\&partnerID=40\&md5=0973327f022946c84a165872a2285524},
	author = {Chen, Y. and Ying, S. and Zhang, L. and Wu, J.}
}
@article {426,
	title = {Robustness analysis of social network based on a dynamic model},
	journal = {Journal of Digital Information Management},
	volume = {11},
	year = {2013},
	note = {Export Date: 10 July 2014},
	month = {2013},
	pages = {453 - 461},
	abstract = {Due to the high vulnerability of today{\textquoteright}s social systems against disruptions, it has become critical to measure and manage the robustness of social networks. In this study the relationship between social network{\textquoteright}s topology and its robustness in the presence of cascading failures is examined. In the attempt to help practice in increasing the robustness of social systems, the concept of the robustness of social systems is discussed and defined first. And then we propose a new dynamic model of social systems to investigate the robustness of social systems. We theorize that network characteristics such as average path length, node degree, and size of the largest connected component in social networks and so on. Based on the model, a good evaluation method is proposed to investigate the robustness of social networks against the cascading failures of component nodes. Finally, the damage degree of social networks considering cascading failures is investigated through our model. The simulation results confirm that there is a positive relationship between the robustness of social systems and social system performance, while a negative relationship between the topology structure and the robustness of social systems. By the dynamic model, we not only identify the potential key nodes, but also obtain the parameter value which can make the social network reach strongest robustness level. Our work develops a new methodology to the real world application. This research is helpful to increase the robustness of such complex systems and tackle the optimization problem of them.},
	keywords = {Disruptions, Graph Theory, Network Damage Degree, Robustness, Social Network Analysis},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84892163854\&partnerID=40\&md5=861e44abb43f2d1f64ca3d9c26ee9c4f},
	author = {Wu, H. and Ying, S. and Jia, X. and Zhang, L. and Chen, X.}
}
