Superordinate knowledge based comprehensive subset of conceptual knowledge for practical mathematical-computational scenarios

The results presented in this paper are based on the research conducted during the last years. Many multi-disciplinary and practical mathematical-computational data and application solutions require to exploit holistically complex scenarios. In many cases, data and algorithms as well as workflows have to be created and tackled individually. The goal of this research is to create an innovative, comprehensive tool base of conceptual knowledge in mathematical-computational application scenarios for arbitrary knowledge context in any media. The solution should be complementary to the commonly available knowledge and features and should fulfill a range of further criteria, especially for a coherent system of knowledge, multi-disciplinary, and data-centric. The result should allow to create and refer to facetted knowledge focussed on mathematical-computational scenarios. The paper presents the results of an implementation based on the fundamental methodology of superordinate knowledge. The solution is targeting mathematical-computational application scenarios and has been used for many practical implementations over more than three decades. The resulting comprehensive subset of conceptual knowledge reference divisions, which was created from this long-term research, is available and first published with this paper.