PIA - A Concept for a Personal Information Assistant for Data Analysis and Machine Learning of Time-Continuous Data in Industrial Applicationsing.grid

Schnur, Christopher; Dorst, Tanja; Schneider, Tizian; Zimmer, Sarah; Müller, Rainer; Margies, Lennard; Deshmukh, Kapil Sajjan; Litzenburger, Philipp; Schütze, Andreas

doi:10.48694/INGGRID.3827

PIA - A Concept for a Personal Information Assistant for Data Analysis and Machine Learning of Time-Continuous Data in Industrial Applicationsing.grid

Schnur, Christopher (Corresponding author) ; Dorst, Tanja ; Deshmukh, Kapil Sajjan ; Zimmer, Sarah ; Litzenburger, Philipp ; Schneider, Tizian ; Margies, Lennard ; Müller, Rainer ; Schütze, Andreas

2023

: Universitäts- und Landesbibliothek Darmstadt

A database with high-quality data must be given to fully use the potential of Artificial Intelligence (AI). Especially in small and medium-sized companies with little experience with AI, the underlying database quality is often insufficient. This results in an increased manual effort to process the data before using AI. In this contribution, the authors developed a concept to enable inexperienced users to perform a first data analysis project with machine learning and record data with high quality. The concept comprises three modules: accessibility of (meta)data and knowledge, measurement and data planning, and data analysis. Furthermore, the concept was implemented as a front-end demonstrator on the example of an assembly station and published on the GitHub platform for potential users to test and review the concept.

Online
DOI: 10.48694/INGGRID.3827
10.48694/INGGRID.3827

Projekte

Inhaltliche Beschreibung (Schlagwörter)
data analysis (Genormte SW) ; machine learning (Genormte SW) ; measurement and data planning (Genormte SW)

Dokumenttyp
Report

Format
online

Herkunft
Externe Einrichtung

Sprache
English

Interne Identnummern
RWTH-2024-06971
Datensatz-ID: 989765

Beteiligte Länder
Germany