Business development or renovation is to introduce newer, more efficient routines and processes through redesign or re-engineering of businesses, which form a set of business patterns. Business patterns encapsulate the best solutions for business practices and tasks confirming business strategies of the enterprise. Nowadays, services with SOA (Service oriented-Architecture) become more and more important in implementing and supporting business routines and processes. An enterprise that can encapsulate their SOA solutions into patterns will make the business more agile and effective. However, with the SOA solutions to automation of locating relevant instance services for its business patterns with minimum human intervention, one has to look into the semantic and operational difference between the description of a business pattern and that of an instance service - a gap between the two levels of descriptions. In this paper, the authors introduce a conceptual modelling method to address how to bridge the gap, by a semantic service description for usage contextual approach formalized with the conceptual graphs formalism. Most importantly, they evaluate this model in this paper to study its usability in practice.
In a service-oriented environment, it is inevitable and indeed quite common to deal with web services, whose reliability is unknown to the users. The reputation system is a popular technique currently used for providing a global quality score of a service provider to requesters. However, such global information is far from sufficient for service requesters to choose the most qualified services. In order to tackle this problem, the authors present a trust based architecture containing a computational trust model for quantifying and comparing the trustworthiness of services. In this trust model, they firstly construct a network based on the direct trust relations between participants and rating similarity in service oriented environments, then propose an algorithm for propagating trust in the social network based environment which can produce personalized trust information for a specific service requester, and finally implement the trust model and simulate various malicious behaviors in not only dense but also sparse networks which can verify the attack-resistant and robustness of the proposed approach. The experiment results also demonstrate the feasibility and benefit of the approach.
The problems of finding best facility locations require complete and accurate road networks with the corresponding population data in a specific area. However the data obtained from road network databases usually do not fit in this usage. In this paper we propose a procedure of converting the road network database to a road graph which could be used for localization problems. Several challenging problems exist in the transformation process which are commonly met also in other data bases. The procedure of dealing with those challenges are proposed. The data come from the National road data base in Sweden. The graph derived is cleaned, and reduced to a suitable level for localization problems. The residential points are also processed in ordered to match the graph. The reduction of the graph is done maintaining the accuracy of distance measures in the network.
With the development of E-commerce and Internet, items are becoming more and more, which brings a so called information overload problem that it is hard for users to find the items they would be interested in. Recommender systems emerge to response to this problem through discovering user interest based on their rating information automatically. But the rating information is usually sparse compared to all the possible ratings between users and items. Therefore, it is hard to find out user interest, which is the most important part in recommender systems. In this paper, we propose a recommendation method TT-Rec that employs trust propagation and topic-level user interest expansion to predict user interest. TT-Rec uses a reputation-based method to weight users' influence on other users when propagating trust. TT-Rec also considers discovering user interest by expanding user interest in topic level. In the evaluation, we use three metrics MAE, Coverage and F1 to evaluate TT-Rec through comparative experiments. The experiment results show that TT-Rec recommendation method has a good performance.