Requirements for Machine Learning Process Software Tooling
Leidner, Jochen L.; Reiche, Michael (2024)
Development Methodologies for Big Data Analytics Systems.
Peer Reviewed
A number of machine learning process models (SEMMA, KDD, CRISP-DM, CRISP-ML, Data-to-Value1 etc.) have been recently proposed to facilitate the development of machine learning models in their organizational context. While the existing proposals vary with respect to complexity and suitability for particular tasks, it would be desirable to have software tools that embody or support these process models, and make it easier for project teams to capture, share among team members and stakeholders and preserve the relevant project information pertaining to the various process stages. In particular, recorded past statistics may be applied to predict the duration of stages or the overall project effort.
Presently, to the best of our knowledge, no requirement analysis exists that stipulates the detailed needs. To this end, we present a first collection and analysis of a requirements document for the software tooling for machine learning process models. We describe the functional and non-functional requirements of a Computer-Aided Machine Learning Modeling (CAMLM) tool, the soft-computing world’s counter-part to a CASE (Computer Aided Software Engineering) tool.
Various software cover sub-areas such as team management and communication management (Confluence, Jira, Slack, Zoom...) or project management (CRISP-DM, Scrum, Kanban-Board...) or data and information management (model management [Weber, Christian; Hirmer, Pascal; Reimann, Peter; Schwarz, Holger (2019): A New Process Model for the Comprehensive Management of Machine Learning Models. In: Proceedings of the 21st International Conference on Enterprise Information Systems: SCITEPRESS - Science and Technology Publications.] ). What is not available to our knowledge, however, is software that covers the entire sub-areas and the entire life cycle of machine learning projects in detail.
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Open Access Peer Reviewed
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Open Access Peer Reviewed
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Open Access
AB0130 profiles of well-being and their relationships with self-forgiveness, forgiveness of others, and gratitude among patients with rheumatic and musculoskeletal diseases. Annals of the Rheumatic Diseases, 83(Suppl 1), 1298-1299. doi:10.1136/annrheumdis-2024-eular.456
Charzyńska , Edyta; Offenbacher, M.; Halverson, k; Hirsch, J. K.; Kohls, Niko; Hanshans, Christian ; Sirios , F. ; Toussaint, L. (2024)
Charzyńska , Edyta; Offenbacher, M.; Halverson, k; Hirsch, J. K.; Kohls, Niko...
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Open Access Peer Reviewed
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Kraft, Jana; Waibl, Paula; Meißner, Karin (2024)
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Open Access Peer Reviewed
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