This article proposes a method of fuzzy induction developed by the author as a combination of the provisions of fuzzy mathematics and fractal theory, introduces the concept of the degree of recursion of a fuzzy set, describes the incomplete recursion of a set as its fractional dimension for modeling a subject domain. As the scope of the proposed method and the knowledge models created on its basis as fuzzy sets, the management of the life cycle of information systems, including the development of scenarios for using and testing software, is considered.
Relevance
In the process of designing and developing, implementing and operating information systems, it is necessary to accumulate and systematize data, information and information that is collected from the outside or arises at each stage of the software life cycle. This serves as the necessary informational and methodological support for design work and decision making, and is especially relevant in situations of high uncertainty and in poorly structured environments. The knowledge base formed as a result of the accumulation and systematization of such resources should not only be a source of useful experience gained by the project team during the work on creating an information system, but also the most simple way of modeling new visions, methods and algorithms for implementing project tasks. In other words,such a knowledge base is a repository of intellectual capital and, at the same time, a knowledge management tool [3, 10].
Efficiency, usefulness, quality of the knowledge base as a tool correlate with the resource intensity of its maintenance and the effectiveness of knowledge extraction. The simpler and faster the collection and fixing of knowledge in the database and the more pertinent the results of queries to it, the better and more reliable the tool itself [1, 2]. Nevertheless, discrete methods and structuring tools that are applicable to database management systems, including normalization of relational database relationships, do not allow to describe or model semantic components, interpretations, interval and continuous semantic sets [4, 7, 10]. To do this, we need a methodological approach that generalizes particular cases of finite ontologies and brings the knowledge model closer to the continuity of the description of the subject area of the information system.
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