Design Patterns for Monitoring and Prediction Machine Learning Systems: Systematic Literature Review and Cluster Analysis

Richard May, Tobias Niemand, Paul Scholz, Thomas Leich

2023

Abstract

Although machine learning methods for industrial maintenance systems have already been well described in recent years, their practical implementation is only slowly taking place. One of the reasons is a lack of comparable analyses of machine learning systems. To address this gap, we first conducted a systematic literature review (2012–2021) of 104 monitoring and prediction systems. Second, we extracted 5 design patterns (i.e., high-level construction manuals) based on a k-means cluster analysis. Our results show that monitoring and prediction systems mainly differ in their choice of operations. However, they usually share similar learning strategies (i.e., supervised learning) and tasks (i.e., classification, regression). With our work, we aim to help researchers and practitioners to understand common characteristics, contexts, and trends.

Paper Citation

in Harvard Style

May R., Niemand T., Scholz P. and Leich T. (2023). Design Patterns for Monitoring and Prediction Machine Learning Systems: Systematic Literature Review and Cluster Analysis. In Proceedings of the 18th International Conference on Software Technologies - Volume 1: ICSOFT; ISBN 978-989-758-665-1, SciTePress, pages 209-216. DOI: 10.5220/0012005800003538

in Bibtex Style

@conference{icsoft23,
author={Richard May and Tobias Niemand and Paul Scholz and Thomas Leich},
title={Design Patterns for Monitoring and Prediction Machine Learning Systems: Systematic Literature Review and Cluster Analysis},
booktitle={Proceedings of the 18th International Conference on Software Technologies - Volume 1: ICSOFT},
year={2023},
pages={209-216},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012005800003538},
isbn={978-989-758-665-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 18th International Conference on Software Technologies - Volume 1: ICSOFT
TI - Design Patterns for Monitoring and Prediction Machine Learning Systems: Systematic Literature Review and Cluster Analysis
SN - 978-989-758-665-1
AU - May R.
AU - Niemand T.
AU - Scholz P.
AU - Leich T.
PY - 2023
SP - 209
EP - 216
DO - 10.5220/0012005800003538
PB - SciTePress