Stable Machine Learning Knowledge Map Domain Analysis.

@InProceedings{10.1007/978-3-030-63128-4_36,
    author="Fayad, Mohamed
    and Kuppa, Gaurav",
    editor="Arai, Kohei
    and Kapoor, Supriya
    and Bhatia, Rahul",
    title="Stable Machine Learning Knowledge Map Domain Analysis",
    booktitle="Proceedings of the Future Technologies Conference (FTC) 2020, Volume 1",
    year="2021",
    publisher="Springer International Publishing",
    address="Cham",
    pages="473--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.",
    isbn="978-3-030-63128-4"
}