Name
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 proper-ties 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 osf.io/zkxv9. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/VIS47514.2020.00032 | 2020 IEEE Visualization Conference (VIS) |
Keywords | DocType | ISBN |
Human-centered computing,Visualization | Conference | 978-1-7281-8015-1 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Neha Makhija | 1 | 0 | 0.34 |
| Mansi Jain | 2 | 0 | 0.68 |
| Tziavelis, Nikolaos | 3 | 1 | 2.04 |
| Laura Di Rocco | 4 | 1 | 3.05 |
| Sara Di Bartolomeo | 5 | 4 | 2.07 |
| Cody Dunne | 6 | 437 | 27.88 |