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Data Analysis Machine Learning and Applications Episode 1 Part 8

Tham khảo tài liệu 'data analysis machine learning and applications episode 1 part 8', kỹ thuật - công nghệ, cơ khí - chế tạo máy phục vụ nhu cầu học tập, nghiên cứu và làm việc hiệu quả | Rationale Models for Conceptual Modeling 161 Derivatives Single Inheritance Multiple Inheritance Inheritance Modelling Fig. 5. Classification of Rationale Fragments reveal that information modeling is characterized by various decision problems. So the choice of the information objects relevant for the modeling problem determines the appropriateness of the resulting model. Furthermore an agreement about the application of certain modeling techniques has to be settled. The branch referring to the usability and utility of the modeling grammar deserves closer attention. Rationale documentations concerning these kinds of issues are not only useful for the model designer and user but they are also invaluable as feedback information for an incremental knowledge base for the designers of the modeling method. Experiences in the method use i.e. usage of the modeling grammar are discovered as an essential resource for the method engineering process cp. Rossi et al. 2004 . Rossi et al. stress these kind of information as a complementary part of the method rationale documentation. They define the method construction rationale and the method use rationale as a coherent unit of rationale information. 4 Conclusion The paper suggests that a classification of design rationale fragments can support the analysis and reuse of modeling experiences resulting in an explicit and systematic structured organizational memory. Owing to the subjectivism in the modeling process the application of an argumentation based design rationale approach could assist the reasoning in design decisions and the reflection of the resulting model. Furthermore Reusable Rationale Blocks are valuable assets for estimating the quality of the prospective conceptual model. The semiformality of the complex rationale models challenges the retrieval of documented discussions relevant to a specific modeling problem. The paper presents an approach for classifying issues by its responding alternatives as a systematic .