Xun Wang

Al-Sharawneh, J.A., Williams, M., Wang, X. & Goldbaum, D. 2011, 'Mitigating Risk in Web-Based Social Network Service Selection: Follow the Leader', International Conference on Internet and Web Applications and Services, St. Maarten, The Netherlands Antilles, March 2011 in The Sixth International Conference on Internet and Web Applications and Services (ICIW 2011), ed Mihhail Matskin, Mark Perry and Zaigham Mahmood,, The International Academy, Research and Industry Association (IARIA), IARIA, pp. 156-164.
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In the Service Web, a huge number of Web services compete to offer similar functionalities from distributed locations. Since no Web service is risk free, this paper aims to mitigate the risk in service selection using 'Follow the Leader' principle as a new approach for risk-reducing strategy. First, we define the user credibility model based on the 'Follow the Leader' principle in web-based social networks. Next we show how to evaluate the Web service credibility based on its trustworthiness and expertise. Finally, we present a dynamic selection model to select the best service with the perceived performance risk and customer risk-attitude considerations. To demonstrate the feasibility and effectiveness of the new 'Follow the Leader' driven approach to alleviate the risk in service selection, we used a Social Network Analysis Studio (SNAS) to verify the validity of the proposed model. The empirical results incorporated in this paper, demonstrate that our approach is a significantly innovative approach as riskreducing strategy in service selection.

Wang, X., Williams, M. 2010, 'A Graphical Model for Risk Analysis and Management', Knowledge Science, Engineering and Management, Belfast, Northern Ireland, September 2010 in Lecture Notes in Artificial Intelligence 6291 - Knowledge Science, Engineering and Management, ed Randy Goebel, Jerg Siekmann, Wolfgang Wahlster, Yaxin Bi and Mary-Anne Williams, Springer-Verlag Berlin Heidelberg, Germany, pp. 256-269.
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Risk analysis and management are important capabilities in intelligent information and knowledge systems. We present a new approach using directed graph based models for risk analysis and management. Our modelling approach is inspired by and builds on the two level approach of the Transferable Belief Model. The credal level for risk analysis and model construction uses beliefs in causal inference relations among the variables within a domain and a pignistic(betting) level for the decision making. The risk model at the credal level can be transformed into a probabilistic model through a pignistic transformation function. This paper focuses on model construction at the credal level. Our modelling approach captures expert knowledge in a formal and iterative fashion based on the Open World Assumption(OWA) in contrast to Bayesian Network based approaches for managing uncertainty associated with risks which assume all the domain knowledge and data have been captured before hand. As a result, our approach does not require complete knowledges and is well suited for modelling risk in dynamic changing environments where information and knowledge is gathered over time as decisions need to be taken. Its performance is related to the quality of the knowledge at hand at any given time.