It's a hot summer in 2021 and the blistering sun is shining upon Madrid. You are enjoying some tinto de verano on your terraza. Sizzling in the scorching heat, you are trying to relax. With a simple gesture you call your robotic assistant to help you cool down a little bit. Without further ado, your robot provides some relaxing shade holding a parasol for you, picks up a fan autonomously and starts waving it and the gentle breeze brings you some light relief.

This paper provides an overview of a research and development project on the automated testing of complex telecommunication networks. The paper reports on the current state of the project, which, for example, includes the development of a database for the organisation and the management of the data that is generated in a network test. Along this line the paper also briefly reports on a future direction in the project, a data warehouse for the management and the organisation of data that is gathered in the wider scope of the project. A major focus of the paper is the proposal and description of a decision support system (DSS). The overall aim of the DSS is to support the decision making of test engineers to further improve the automated testing of complex telecommunication networks.

This paper compares three different data analysis methods in a subfield of the coronary heart disease risk assessment (CHDRA) area—the identification of increased blood cholesterol levels. A data set containing the cholesterol data of 166 persons is employed as a test case, and analyzed in three experimental investigations. The first analysis method employs a predominantly knowledge-based fuzzy expert system solution to the problem. The second method employs statistical discriminant analysis on the same data, whereas the results of the third analysis are obtained by an artificial neural network. This study evaluates the advantages as well as the disadvantages of each method. Special attention is given to the systems' individual capability for managing the uncertainty involved in the decision-making process. The results achieved in the study provide evidence for the complementary and mutually supporting character of the different approaches. ©2000 John Wiley & Sons, Inc.