Artikel in Tagungsband INPROC-2018-54

Bibliograph.
Daten
Villanueva Zacarias, Alejandro; Reimann, Peter; Mitschang, Bernhard: A Framework to Guide the Selection and Configuration of Machine-Learning-based Data Analytics Solutions in Manufacturing.
In: Proceedings of the 51st CIRP Conference on Manufacturing Systems (CIRP CMS 2018).
Universität Stuttgart, Fakultät Informatik, Elektrotechnik und Informationstechnik.
S. 153-158, englisch.
Elsevier BV, Mai 2018.
Artikel in Tagungsband (Konferenz-Beitrag).
CR-Klassif.H.2.8 (Database Applications)
Keywordsdata analytics; machine learning; learning algorithms; generative design
Kurzfassung

Users in manufacturing willing to apply machine-learning-based (ML-based) data analytics face challenges related to data quality or to the selection and configuration of proper ML algorithms. Current approaches are either purely empirical or reliant on technical data. This makes understanding and comparing candidate solutions difficult, and also ignores the way it impacts the real application problem. In this paper, we propose a framework to generate analytics solutions based on a systematic profiling of all aspects involved. With it, users can visually and systematically explore relevant alternatives for their specific scenario, and obtain recommendations in terms of costs, productivity, results quality, or execution time.

Abteilung(en)Universität Stuttgart, Institut für Parallele und Verteilte Systeme, Anwendersoftware
Projekt(e)GSaME-NFG
Eingabedatum26. Juli 2019
   Publ. Abteilung   Publ. Institut   Publ. Informatik