Under CopyrightAlthoff, K.-D.K.-D.AlthoffDecker, B.B.DeckerHartkopf, S.S.HartkopfJedlitschka, A.A.JedlitschkaNick, M.M.NickRech, J.J.Rech2022-03-0724.04.20022001https://publica.fraunhofer.de/handle/publica/29108410.24406/publica-fhg-291084Experience Management (EM) is an area that is increasingly gaining importance. Its roots lie in Experimental Software Engineering ("Experience Factory"), in Artificial Intelligence ("Case-Based Reasoning"), and in Knowledge Management. EM is comprised of the dimensions methodology, technical realization, organization, and management. It includes technologies, methods, and tools for identifying, collecting, documenting, packaging, storing, generalizing, reusing, adapting, and evaluating experience knowledge, as well as for development, improvement, and execution of all knowledge-related processes. The main difference between experience knowledge and general knowledge is the fact that normally, a (more or less) continuous "stream of knowledge" must be processed. Within this paper, we present some basic methods of EM, using the Fraunhofer IESE Experience Factory as an example, which, after a one-year trial run, has been in regular operation since the beginning of this year.1 Introduction S.1-2 2 Experience Management S.3-6 - 2.1 Case-Based Reasoning S.4 - 2.2 Experience Factory S.5-6 Table of Contents S.7 3 IESE Corporate Information Network: The Experience Factory S.7-8 4 Experience Base Buildup Method S.9-10 5 The Experience Management Content Framework of the EB S.11-12 6 Managing Business Processes and Lessons Learned as Experiences S.13-17 - 6.1 Business Process Descriptions S.13-14 - 6.2 Capturing and Presenting Lessons Learned S.15-17 7 Maintenance S.18-22 - 7.1 Overview on EB/CBR Maintenance Knowledge Types S.19 - 7.2 Acquiring Maintenance Decision Knowledge S.20 - 7.3 Tool Support for Maintenance Decision Making S.21-22 8 New Strategies for Capturing, Process, Disseminate and Exchange Knowledge S.23-26 - 8.1 ?Push? of Information/Knowledge S.23 - 8.2 Community of Practice Base (CoP) S.24-25 - 8.3 Aggregation and Adaptation of Information S.26 9 Data Mining in Experience Bases S.27-29 - 9.1 Knowledge Discovery in Experience Bases / COIN S.27 - 9.2 Experience Base Construction and Usage Support S.28 - 9.3 Quality of knowledge / experience S.28-29 - 9.4 Support of other strategies S.29 10 Summary S.30 11 References S.31en004005006report