ADEQUATe – Analytics & Data Enrichment to improve the Quality of Open Data
The ADEQUATe project will identify the requirements for the quality of open data management in Austria and evaluate the mechanisms needed to monitor, evaluate and solve the identified data quality issues on open data portals and beyond. Building on these findings, the project will develop data quality mechanisms in 3 areas:
- automated machine-driven mechanisms,
- data linkage, and crowdsourcing approaches,
- combining these results into the (Open) Data Quality Monitoring and Evaluation Framework.
This Framework will be deployed in 2 real world use cases, pertaining to the major open data portals in Austria: data.gv.at and opendataportal.at. It will be evaluated and refined in several iterations to follow an agile software development process along user needs (user-driven & data-driven development).
An ever increasing amount of Open Data becomes available as an important resource for emerging businesses. The integration of such open, freely re-usable data sources into organisations’ data warehouse and data management systems will be a key success factor for a competitive advantage in a data-driven economy. The crucial issues to be tackled for fully exploiting the value of open data and the efficient integration with other data sources are:
- overall quality issues with meta data and the data itself and
- the lack of interoperability between data sources
The crucial part is that these issues need to be already addressed when open data is provided by either governmental organisations or other stakeholders publishing open data.
Data quality in Open Data Publishing was not addressed as a major issue in the open data movement so far, but becomes crucial these days as the re-use of open data is immensely increasing.
The consortium will research and develop novel automated and community-driven data quality improvement techniques which will be integrated into existing Open Data portals, thereby increasing the overall value for consumers; interlinking will contribute to the potential of reusing data sources by creating a network of interconnected Open Data that can be easily integrated.
A quality assessment & monitoring framework in combination with questionnaires will continuously evaluate and demonstrate the impact of the ADEQUATe solutions.
- Semantic Web Company GmbH
- Donau-Universität Krems
- Wirtschaftsuniversität Wien