Constructing reusable knowledge for machine learning projects based on project practices

Issue title: Novel Research Results Presented at the JCKBSE2022

Guest editors: George A. Tsihrintzis, Maria Virvou and Takuya Saruwatari

Article type: Research Article

Authors: Takeuchi, Hironori^{a; *} | Imazaki, Kota^b | Kuno, Noriyoshi^c | Doi, Takuo^d | Motohashi, Yosuke^e

Affiliations: [a] Musashi University, Tokyo, Japan | [b] Information-Technology Promotion Agency, Japan | [c] Mitsubishi Electric Corporation, Japan | [d] Digital Athlete, Japan | [e] NEC Corporation, Japan

Correspondence: [*] Corresponding author: Hironori Takeuchi, Musashi University, Toyotama-kami 1-26-1, Nerima-ku, Tokyo 176-8534, Japan. E-mail: [email protected].

Abstract: Recently, machine learning (ML) techniques have been introduced into various domains. This study focuses on projects for the development of ML-based service systems in which ML techniques are applied to enterprise functions. In these projects, constructing reusable knowledge on projects that develop ML-based service systems is important to effectively implement such projects. Here, the collection of insights and development of architecture and design patterns for ML-based service systems are considered. We propose a method for collecting insights by referring to a development model based on project practices and developing patterns for ML projects as an enterprise architecture model. Through a practice, we attempt to collect insights as best practices and construct design patterns for ML projects using the proposed method.

Keywords: Machine learning, ML service system, best practice, pattern

DOI: 10.3233/IDT-220252

Journal: Intelligent Decision Technologies, vol. 16, no. 4, pp. 725-735, 2022