A GENERIC SHELL APPROACH FOR KMOWLEDGE ELICITATION AMD REPRESENTATION IM IDSS

This study focuses on issues of knowledge representation and elicitation in Intelligent DSS (IDSS) environments. The types, characteristics, levels of logical view, and the levels of specificity and abstraction of passive and active knowledge in IDSS are discussed. A language for knowledge description, whose syntactical objects are entities, relationships, transformations, and constraints, and which allows four levels of specificity and abstraction is proposed. Then, a graphical, semantic model for the conceptual-schema representation of passive and active knowledge, called the extended ERA Model, is presented. Finally, it is argued that a multi-paradigm programming environment is required for the information-schema representation of the different types of knowledge in IDSS, and to support reasoning, inference, and inheritance. A LOOPS implementation of the knowledge representation and elicitation model is described in detail

Multiobjective optimization for interwoven systems

In practical situations, complex systems are often composed of subsystems or subproblems with single or multiple objectives. These subsystems focus on different aspects of the overall system, but they often have strong interactions with each other and they are usually not sequentially ordered or obviously decomposable. Thus, the individual solutions of subproblems do not generally induce a solution for the overall system. Here, we strive to identify "re-composition architectures" of such "interwoven" systems. Our intention is to connect the subsystems adequately, analyze the resulting performance, model/solve the overall system, and improve the overall solution instead of just solving each subsystem separately. We review recent developments in this field and discuss modeling and solution paradigms in a general and unified framework using the example of an interwoven system consisting of two interacting subsystems

Synthesis of best practices for the development of an integrated data and information management approach

105 p.State transportation agencies deal with numerous technical and organizational challenges in building integrated data and information systems to provide high-quality information for supporting analysis, control and decision making in asset management. There is a need to identify the approaches that work and to develop guidelines for agencies working to achieve data integration. This project seeks to identify relevant metrics for performance measurement and the underlying factors and best practices that contribute to successful approaches. The research was comprised of six tasks: literature review; design of data collection instruments; pilot testing of data collection instruments; collection of data - interviews; identification and analysis of key success factors, best practices, and performance measures; and preparation of the guidelines document. Comprehensive case studies on data integration issues and best practices regarding asset management and information systems development were conducted on the Wisconsin, Michigan and Ohio Departments of Transportation. All data integration efforts must be business driven. Without a business imperative for investing in systems development, there is no incentive for people to learn or implement the new system. Successful development of asset management systems require constant involvement of both business and technology people and a great deal of communication with both end users and users of the business systems being integrated