Loch Prospector: Metadata visualization for lakes of open data

  author    = {Makhija, Neha and Jain, Mansi and Tziavelis, Nikolaos and Di Rocco, Laura and Di~Bartolomeo, Sara and Dunne, Cody},
  booktitle = {Proc. IEEE Visualization Conference},
  title     = {{Loch Prospector}: Metadata visualization for lakes of open data},
  year      = {2020},
  month     = {Oct},
  note      = {Preprint \& supplemental material: https://osf.io/2s76d},
  pages     = {126-130},
  series    = {VIS},
  abstract  = {Data lakes are an emerging storage paradigm that promotes data availability over integration. A prime example are repositories of Open Data which show great promise for transparent data science. Due to the lack of proper integration, Data Lakes may not have a common consistent schema and traditional data management techniques fall short with these repositories. Much recent research has tried to address the new challenges associated with these data lakes. Researchers in this area are mainly interested in the structural properties of the data for developing new algorithms, yet typical Open Data portals offer limited functionality in that respect and instead focus on data semantics. We propose Loch Prospector, a visualization to assist data management researchers in exploring and understanding the most crucial structural aspects of Open Data — in particular, metadata attributes — and the associated task abstraction for their work. Our visualization enables researchers to navigate the contents of data lakes effectively and easily accomplish what were previously laborious tasks. A copy of this paper with all supplemental material is available at https://osf.io/zkxv9},
  doi       = {10.1109/VIS47514.2020.00032},

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