Visually Exploring Data Provenance and Quality of Open Data
| dc.contributor.author | Bors, Christian | en_US |
| dc.contributor.author | Gschwandtner, Theresia | en_US |
| dc.contributor.author | Miksch, Silvia | en_US |
| dc.contributor.editor | Anna Puig and Renata Raidou | en_US |
| dc.date.accessioned | 2018-06-02T17:55:42Z | |
| dc.date.available | 2018-06-02T17:55:42Z | |
| dc.date.issued | 2018 | |
| dc.description.abstract | 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. | en_US |
| dc.description.sectionheaders | Posters | |
| dc.description.seriesinformation | EuroVis 2018 - Posters | |
| dc.identifier.doi | 10.2312/eurp.20181117 | |
| dc.identifier.isbn | 978-3-03868-065-9 | |
| dc.identifier.pages | 9-11 | |
| dc.identifier.uri | https://doi.org/10.2312/eurp.20181117 | |
| dc.identifier.uri | https://diglib.eg.org:443/handle/10.2312/eurp20181117 | |
| dc.publisher | The Eurographics Association | en_US |
| dc.subject | Information systems | |
| dc.subject | Data cleaning | |
| dc.subject | Data analytics | |
| dc.subject | Human | |
| dc.subject | centered computing | |
| dc.subject | Visual analytics | |
| dc.title | Visually Exploring Data Provenance and Quality of Open Data | en_US |