Читать книгу Handbook on Intelligent Healthcare Analytics - Группа авторов - Страница 12

D. Karthika* and K. Kalaiselvi†

Department of Computer Applications, Vels Institute of Science, Technology & Advanced Studies (Formerly Vels University), Chennai, Tamil Nadu, India

Abstract

In recent years, the philosophy of Knowledge Engineering has become important. Information engineering is an area of system engineering which meets unclear process demands by emphasizing the development of knowledge in a knowledge-based system and its representation. A broad architecture for knowledge engineering that manages the fragmented modeling and online learning of knowledge from numerous sources of information, non-linear incorporation of fragmented knowledge, and automatic demand-based knowledge navigation. The project aims to provide petabytes in the defined application domains with data and information tools. Knowledge-based engineering (KBE) frameworks are based on the working standards and core features with a special focus on their built-in programming language. This language is the key element of a KBE framework and promotes the development and re-use of the design skills necessary to model complex engineering goods. This facility allows for the automation of the process preparation step of multidisciplinary research (MDA), which is particularly important for this novel. The key types of design rules to be implemented in the implementation of the KBE are listed, and several examples illustrating the significant differences between the KBE and the traditional CAD approaches are presented. This chapter discusses KBE principles and how this technology will facilitate and enable the multidisciplinary optimization (MDO) of the design of complex products. This chapter discusses their reach goes beyond existing CAD structure constraints and other practical parametric and space exploration approaches. There is a discussion of the concept of KBE and its usage in architecture that supports the use of MDO. Finally, this chapter discusses on the key measures and latest trends in the development of KBE.

Keywords: Data analytics, knowledge, knowledge engineering, principles, knowledge acquisition