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C. Bors, T. Gschwandtner, S. Miksch:
"Visually Exploring Data Provenance and Quality of Open Data";
Poster: Eurographics / IEEE VGTC Conference on Visualization (EuroVis 2018), Brünn; 2018-06-04 - 2018-06-08; in: "Posters", The Eurographics Association, (2018), 9 - 11.

While open data platforms are increasingly popular among end-users as well as data providers, there is a growing problem with inconsistent update frequencies and lack of quality in datasets. Efforts to monitor data quality are currently limited to checking meta-information and creating revisions to allow manual inspection of former datasets. We employ a Visual Analytics framework for generating and visualizing data provenance from data quality to facilitate data analysis and help users to understand the impact of updates on the data. Data quality metrics are utilized to quantify the development of data quality over time for open data projects. We combine quality metrics, data provenance, and data transformation information in an interactive exploration environment to expedite assessment and selection of appropriate open datasets.

Data cleaning, Data analytics, Visual analytics, data provenance

http://dx.doi.org/10.2312/eurp.20181117

https://publik.tuwien.ac.at/files/publik_270179.pdf

Project Head Silvia Miksch:
CVAST: Centre for Visual Analytics Science and Technology (Laura Bassi Centre of Expertise)