Classification Methodology for Architectures in Information Systems: A Statistical Converging Technique

Presenter Information

Qishan Yang, Dublin City University, Dublin, IrelandFollow
Mouzhi Ge, Deggendorf Institute of Technology, Deggendorf, GermanyFollow
Markus Helfert, Maynooth University, Maynooth, IrelandFollow

Abstract

Architectures are critical to the Information System (IS) domain because they represent funda- mental structures and interactions of systems. Since analysing architecture similarities is chal- lenging and time-consuming even in one domain, IS architecture classifications are paramount to understanding architectural complexity. However, classification approaches used in existing research commonly rely on manual interventions, and thus architectural classification reliability is hampered. We propose a novel methodology based on component modelling and applica- tion of a statistical converging technique, which ensures reliable IS architectural classification and minimises subjective interventions. We demonstrate the methodology by classifying data warehouse architectures.

Recommended Citation

Yang, Q., Ge, M., & Helfert, M. (2023). Classification Methodology for Architectures in Information Systems: A Statistical Converging Technique. In A. R. da Silva, M. M. da Silva, J. Estima, C. Barry, M. Lang, H. Linger, & C. Schneider (Eds.), Information Systems Development, Organizational Aspects and Societal Trends (ISD2023 Proceedings). Lisbon, Portugal: Instituto Superior Técnico. ISBN: 978-989-33-5509-1. https://doi.org/10.62036/ISD.2023.11

Paper Type

Full Paper

DOI

10.62036/ISD.2023.11