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SFB/Transregio 161 Teilprojekt D03 SFB/Transregio

Visual exploration and analysis of provenance data
ProjekttypSFB/Transregio
Gefördert durch DFG - SFB/Transregio 161
Beginn 01.07.2015
Ende
Leiter Prof. Dr. rer. nat. Melanie Herschel
Mitarbeiter Ben Lahmar, Houssem
Ansprechpartner
Kurzbeschreibung

To analyze or debug complex data processing applications, or to ensure their understandability and repeatability, provenance techniques are increasingly being deployed, resulting in large volumes and a wide variety of provenance data. The long-term goal of this project is to leverage visualization techniques to efficiently and effectively explore provenance data. In this context, we plan to address the following research questions that will ultimately allow us to (i) make provenance data more accessible and useful, especially in applications performing complex data processing on vast amounts of data by providing suited visualization techniques and (ii) show how data transformations applied in visual computing can benefit from provenance information.

  • Provenance data visualisation: We plan to identify suitable visualization techniques for the different settings defined by the varying types of provenance and applications.
  • Quantification of Provenance Data Visualisation Quality: We will identify metrics to quantitatively assess provenance data visualization quality. These metrics will further serve as a basis for developing a configuration advisor tool that can forecast the overall system performance and that can be used to optimally configure provenance data generation given a set of requirements.
  • Provenance for visual computing reproducibility and predictability: We will contribute a general and systematic approach to using provenance for reproducible and predictable visual computing applications. More precisely, we will study quantitatively which types of prove- nance are best suited to achieve the goals of reproducibility and predictability for selected visual computing techniques. Eventually, this will contribute to defining a general framework for exploiting provenance for reproducibility and predictability in visual computing pipelines.
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