With the wide availability of products and services through popular e-commerce platforms and dozens of similar offerings to choose from, there is a need to accurately assess and evaluate the quality of offerings. Several studies have shown that consumer feedback is an important source of information. This paper presents: (a) consumer Rating as a Service (RaaS) -- a building block service that can be used to add the consumer feedback lifecycle feature in the development of e-commerce platforms, (b) an approach to evaluate the quality of composite offerings based on the aggregation of consumer ratings using the composition structure and component relationships. Benefits of the proposed service include reduced development effort, shorter delivery time and a fine-grained aggregation of consumer ratings for composite offerings even with limited ratings.

Model-driven architectures (MDA) separate the business or application logic from the underlying platform technology and represent this logic with precise semantic models. These models are supposed to span the entire life cycle of a software system and ease the software production and maintenance tasks. Consequently, tools will be needed that support these tasks. In this paper, we present a method that implements model-driven transformations between particular platform-independent (business view) and platform-specific (IT architectural) models. On the business level, we focus on business view models expressed in ADF or UML2, whereas on the IT architecture side we focus on service-oriented architectures with Web service interfaces and processes specified in business process protocol languages such as BPEL4WS.

We describe techniques for combining two types of knowledge systems: expert and machine learning. Both the expert system and the learning system represent information by logical decision rules or trees. Unlike the classical views of knowledge-base evaluation or refinement, our view accepts the contents of the knowledge base as completely correct. The knowledge base and the results of its stored cases will provide direction for the discovery of new relationships in the form of newly induced decision rules. An expert system called SEAS was built to discover sales leads for computer products and solutions. The system interviews executives by asking questions, and based on the responses, recommends products that may improve a business' operations. Leveraging this expert system, we record the results of the interviews and the program's recommendations. The very same data stored by the expert system is used to find new predictive rules. Among the potential advantages of this approach are (a) the capability to spot new sales trends and (b) the substitution of less expensive probabilistic rules that use database data instead of interviews.