Publikation

Systematic Maintenance for Corporate Experience Repositories

Markus Nick, Klaus-Dieter Althoff, Carsten Tautz

In: Computational Intelligence (CI) 17 2 Seiten 364-386 Blackwell Publishers Oxford, UK 5/2001.

Abstrakt

Experience-based continuous learning is essential for improving products, processes, and technologies in emerging as well as established areas of business and engineering science. This can be facilitated by case-based organizational learning through capturing relevant experience in the form of cases for reuse in a corporate experience repository. For obvious reasons, learning from experience needs to be a permanent endeavor. Thus, an organization has to handle a “continuous stream of experience”. Consequently, such an experience repository requires maintenance. This should not simply happen ad-hoc, but in a systematic manner. To make competent decisions about maintenance, the experience base and its usage have to be analyzed (i.e., evaluated). To improve maintenance itself, it is necessary to learn about it. For this purpose, we identify the relevant tasks for maintaining an experience repository and the responsibilities of the roles involved. Maintenance addresses not only the actual experience in the form of cases, but also the conceptual model and the methods, techniques, and tools that are used for filling and updating the experience repository. To support the roles involved in the maintenance tasks, we provide a flexible, practical maintenance and evaluation framework. This framework provides guidance for the roles. The framework can be combined with other approaches from artificial intelligence, knowledge engineering, and software engineering at different levels of detail. For the practical application of the framework, we describe an integrated technical solution for a corporate experience repository that is maintained using our framework. Furthermore, we discuss the empirical validation of the framework and its implementation.

Weitere Links

Nick_et_al-2001-Computational_Intelligence.pdf (pdf, 528 KB)

Deutsches Forschungszentrum für Künstliche Intelligenz
German Research Center for Artificial Intelligence