Author ORCID Identifier
Jannis Walk: 0000-0002-8410-1065
Max Schemmer: 0000-0001-6341-2051
Niklas Kühl: 0000-0001-6750-0876
Gerhard Satzger: 0000-0001-8731-654X
Abstract
Many decision processes are based on image analysis, for instance, medical diagnoses or visual monitoring of industrial processes. At the same time, advances in deep learning have significantly improved information extraction from images. While recent research strongly focuses on extracting information from single images, the potential of mining entire image collections for decision processes has been neglected so far. In this work, we develop design knowledge to use image collections for improved decision-making. We derive design requirements for image-mining-based decision support systems from literature and expert interviews. Drawing on research in image mining and decision support systems, we conceptualize design principles to address the design requirements. Subsequently, we instantiate and evaluate them in the machining industry with the help of an artifact to support tool wear analysis. The results prove the validity of our design knowledge. Our study contributes to research and practice by deriving nascent design knowledge for image-mining-based decision support systems.
DOI
10.17705/1CAIS.05447
Recommended Citation
Walk, J., Schemmer, M., Kühl, N., & Satzger, G. (2024). Image-Mining-Based Decision Support Systems: Design Knowledge and its Evaluation in Tool Wear Analysis. Communications of the Association for Information Systems, 54, 1124-1152. https://doi.org/10.17705/1CAIS.05447
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