Model based systems engineering—A text mining based structured comprehensive overview

Abstract

An observed increase in systems scale and complexity has led to a significant momentum in exploring, identifying, and adopting model based systems engineering (MBSE) tools and techniques amongst research communities and industry practitioners. Several attempts to transform systems design and engineering practices through the use of MBSE in academia and industry has led to a considerable increase in the number of articles published containing the keyword "MBSE." This growth serves as the motivation in this paper to explore the MBSE landscape with the help of text mining techniques to identify the most often used key terms, tools, and languages, in the context of research in MBSE and the thematic aspects defining the use of MBSE by researchers and practitioners. The objective of this paper is to provide a structured comprehensive overview of research contributions across the MBSE landscape by employing text mining techniques for: (a) identifying the concepts and methodologies inferred upon in relation to MBSE, and (b) classifying the literature published to identify commonalities across academic researchers and practitioners using MBSE tools and methods.For this purpose, the abstracts of 2380 relevant articles published in the period of the last two decades from five different databases are mined. It is found that the terms "SysML," "Cyber Physical Systems," and "Production" are the most used terms among researchers across the MBSE landscape with SysML being the most widely used modeling language. Further, six major thematic topics are identified that classify articles from over the last two decades with an increasing interest observed in the use of MBSE to support manufacturing and production engineering activities, especially in the cyber physical systems domain. The contributions of this paper provide a leeway on using text mining techniques to understand the research directions that are currently of interest in the field of MBSE and thereby identify potential future research directions.

State of Industry 5.0—Analysis and Identification of Current Research Trends

“…It has been widely recognized among researchers that text mining's feasibility for exploring published literature and discovering concepts and trends across a given domain has seen tremendous growth. For example, Bach et al discuss the advantage of using text mining in the financial sector for stock market predictions [13], Aureli portrays the applicability of text mining for studying organizations' social and environmental reports [14], Namugera et al use text mining to study the social media usage of traditional media houses in Uganda to understand the topics these media houses discuss and determine if they are positively or negatively correlated [15], and use of text mining tools and techniques to analyze the landscape of Model based Systems Engineering [16].…”

Bibliometric Analysis of Model-Based Systems Engineering: Past, Current, and Future

“…A few studies have comprehensively reviewed MBSE using bibliometrics, but limitations still exist. For example, Akundi et al [61] focused on the current research status through text mining of a large number of MBSE literature abstracts but did not provide a meticulous analysis of the current humanistic cooperation, journals, and future development. Li et al [62] thoroughly explored MBSE using the bibliometric method.…”

Text Mining-based Enterprise Financial Performance Evaluation in the Context of Enterprise Digital Transformation

“…Akundi et al observed a significant increase in academic and industrial research in the field of systems engineering and proposed a comprehensive and structured integration of research in the field through the use of text mining techniques in order to understand the existing research directions in the field. "system modelling language", "physical system" and "production" are the most used terms in systems engineering research, with system modelling language being the most widely used modelling language [11]. Leem et al propose a text mining approach to sentiment analysis of customers' online evaluations for Kakao mobile banking service, which is ambiguous and unclear.…”