摘要：Aspect-oriented modeling can uncover potential design faults, yet most existing work fails to achieve both separation and composition in a natural and succinct way. This study presents an aspect-oriented modeling and analysis approach with hierarchical Coloured Petri Nets(HCPN). HCPN has sub-models and well-defined semantics combining a set of submodels. These two characteristics of HCPN are nicely integrated into aspect oriented modeling. Submodels are used to model aspects while the combination mechanism contributes to aspects weaving. Furthermore, the woven aspect oriented HCPN model can be simulated and analyzed by the CPN Tools. A systematic web application case study is conducted. The results show the system original properties are satisfied after weaving aspects and all design flaws are revealed. As such, the approach can support web application design and analysis in an aspect-oriented fashion concisely and effectively.

摘要：The stimulus to carry out this research is to investigate the relationship between internet advertisement and its features on the total E-commerce sales of the top five countries of Europe. The units of analysis are the individuals of UK, France, Italy, Germany and Netherland. Secondary data are collected from the reports of [1] (ADEX, 2010) and [2] (Eurostats, 2011). To empirically determine the relationship between independent variable and dependent variable in the European context, the study uses various statistical techniques, including OLS regression and correlation analysis techniques. The empirical findings indicate that the Internet advertisement features of search advertisement and classified advertisement have positive significant relationship with the E-commerce sales in Europe. The empirical findings indicate negative significant relationship of display advertisement with the E-commerce sales in Europe. However, this variable is also justified with the help of literature. Findings also demonstrate that search advertisement has strong positive relationship and it generates positive influence for the E-commerce sales as compared to the classified advertisement and display advertisement. Firms and marketers which are investing in online advertisement will find these results useful as they can get better sales and can use these features of online advertisement in order to maximize the sales of their products and services.

摘要：In this study, a novel hybrid intelligent mining system integrating rough sets theory and support vector machines is developed to extract efficiently association rules from original information table for credit risk evaluation and analysis. In the proposed hybrid intelligent system, support vector machines are used as a tool to extract typical features and filter its noise, which are different from the previous studies where rough sets were only used as a preprocessor for support vector machines. Such an approach could reduce the information table and generate the final knowledge from the reduced information table by rough sets. Therefore, the proposed hybrid intelligent system overcomes the difficulty of extracting rules from a trained support vector machine classifier and possesses the robustness which is lacking for rough-set-based approaches. In addition, the effectiveness of the proposed hybrid intelligent system is illustrated with two real-world credit datasets.