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@TECHREPORT{Schnur:989765,
      author       = {Schnur, Christopher and Dorst, Tanja and Deshmukh, Kapil
                      Sajjan and Zimmer, Sarah and Litzenburger, Philipp and
                      Schneider, Tizian and Margies, Lennard and Müller, Rainer
                      and Schütze, Andreas},
      title        = {{PIA} - {A} {C}oncept for a {P}ersonal {I}nformation
                      {A}ssistant for {D}ata {A}nalysis and {M}achine {L}earning
                      of {T}ime-{C}ontinuous {D}ata in {I}ndustrial
                      {A}pplicationsing.grid},
      publisher    = {Universitäts- und Landesbibliothek Darmstadt},
      reportid     = {RWTH-2024-06971},
      year         = {2023},
      note         = {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.},
      keywords     = {data analysis (Other) / machine learning (Other) /
                      measurement and data planning (Other)},
      pnm          = {NFDI4Ing Task Area ALEX - Task Area ALEX - bespoke
                      experiments with high variability of setups (442146713-ALEX)
                      / DFG project 442146713 - NFDI4Ing – Nationale
                      Forschungsdateninfrastruktur für die
                      Ingenieurwissenschaften (442146713)},
      pid          = {G:(GEPRIS)442146713-ALEX / G:(GEPRIS)442146713},
      typ          = {PUB:(DE-HGF)29},
      doi          = {10.48694/INGGRID.3827},
      url          = {https://publications.rwth-aachen.de/record/989765},
}