System Engineering (SE) is becoming increasingly knowledge intensive. Knowledge Management is recognized as a crucial enabler for continuous process improvement in engineering projects. Particularly, capitalization and sharing, of knowledge resulting from experience feedback are valuable asset for SE companies. In this paper, we focus on the formalization of engineering experience aiming at transforming information or understanding gained by projects into explicit knowledge. A generic SE ontological framework acts as a semantic foundation for experience capitalization and reuse. This framework is operationalized with Conceptual Graphs formalism and applied to a transport system engineering use case.
Service oriented architectures (SOAs) are a new paradigm in IT-management. This paper presents an SOA-implementation in the intranet portal of a 520-employee research institute, which was launched to accompany parallel knowledge-management (KM) activities. Furthermore it explains the benefits of an SOA-based IT-landscape for KM-activities.
Adapting E-Learning material allows re-using existing material even in changed usage scenarios. But this adaptation is a complex task. To achieve a perfect result expert knowledge is needed. Often adaptations have to be performed by persons who are not experts in performing these tasks. To enable those persons to achieve a satisfying result they need to be supported. Patterns are one possibility to make expert knowledge on certain tasks available to other persons. In this paper an approach is presented how expert knowledge of performing adaptations of existing E-Learning material can be collected and made available with patterns. This approach can also be used to collect knowledge of other processes, e.g. in companies.
Today, reuse-oriented software engineering covers the process of the development and evolution of software systems by reusing existing experience (i.e., products, processes, and knowledge). One of the major problems of software reuse is the lack of knowledge and skills for understanding reusable experience. This paper explains how the reuse process can be used to support individual learning on the one hand, and how learning can improve the selection of reuse experience and their application on the other hand. The paper emphasizes the importance
of context in the domain of reuse and how context information can be used to compose socalled Learning Spaces from Learning Components. Learning Spaces didactically enrich reusable experience and enhance experiential learning. The approach uses Wikis as a base technology for presenting and structuring learning content.
Knowledge discovery in data sources available on Computational Grids is a challenging research and development issue. Several Grid research activities addressing some facets of this process have already been reported. The GridMiner project (a joint work between the University of Vienna and Vienna University of Technology) aims, as the first Grid research effort, to cover all aspects of the knowledge discovery process and integrate them into an advanced service-oriented Grid application. The innovative architecture provides (1) a robust and reliable high performance data mining and OLAP environment (2) seamless access to intermediate data and results of the discovery process for further reuse in a standardized way (3) a persistant workspace for continuous and evolving data mining tasks supported via a flexible GUI. The interactive cooperation of different services – for data integration, data selection, data transformation, data mining, pattern evaluation, knowledge presentation and finally its storage – within the GridMiner architecture is the key to high performance knowledge discovery in large datasets.
Procedural knowledge is one of the most valuable assets of individuals as well as academic institutions and commercial companies. The ability to satisfy an order relies on the knowledge how similar tasks have been performed in the past. Thus the preservation of this knowledge is critical. Procedural knowledge takes many different forms, which makes it very hard to reason about it. We propose a method to reduce it to its very essence. This method is very simple, and as such it is not new. But we argue that it is worthwhile to take a fresh look on an existing technology from a new point of view, because it may solve the problem of knowledge preservation that has become apparent in this form only recently. Although the technique is known for a long time, it appears that its potential for the management of procedural knowledge has not been realized so far. It is a also very elegant method since we can show that it serves both as a theoretical device to better understand the nature of processes, but it can also be directly operationalized to derive a new generation of user-friendly tools that support the preservation of procedural knowledge.