Stable Machine Learning Knowledge Map Domain Analysis
Publication Date
1-1-2021
Document Type
Conference Proceeding
Publication Title
Advances in Intelligent Systems and Computing
Volume
1288
DOI
10.1007/978-3-030-63128-4_36
First Page
473
Last Page
483
Abstract
Stable patterns that are widely used in today’s software engineering in modelling and it plays an important role in reducing the cost and condensing the time of software product lifecycles. Nowadays, many existing traditional patterns fail to model the subtle changes in context of the implementation of the model. As a result, the reusability of the pattern will significantly decrease. The goal of this paper is to present a pattern language for building a core knowledge of stable patterns called knowledge map. This paper will also represent the first attempt towards a machine learning knowledge map representation via stable patterns as a mean to discover, organize, and utilize machine learning core knowledge. Each stable pattern focuses on a distinctive activity and provides a way by which this activity can be conducted efficiently. The presented stable analysis and design patterns will provide a core knowledge of stable machine learning domain that is easily extensible, stable through time, and focus on stable machine learning of Unified (1) Functional and non-Functional Requirements (2) Unified Design.
Keywords
Business Objects (BO), Domain analysis, Enduring Business Themes (EBT), Industrial Objects (IO), Stable Machine Learning, Stable model
Department
Computer Engineering
Recommended Citation
Mohamed Fayad and Gaurav Kuppa. "Stable Machine Learning Knowledge Map Domain Analysis" Advances in Intelligent Systems and Computing (2021): 473-483. https://doi.org/10.1007/978-3-030-63128-4_36
