The ADEQUATe project develops methods to measure, monitor, and improve data quality in open data portals. Statistical, algorithmic, linked data, and crowdsourcing approaches are combined in a data quality monitoring framework. These methods are evaluated and further developed on the data.gv.at and opendataportal.at portals. The goals are to define quality measures, monitor quality evolution over time, and improve quality through algorithms, linking data to other open datasets, and crowdsourcing.

1. ADEQUATeImproving Data Quality in Open Data
http://www.adequate.at

2. Im ADEQUATe Projekt werden Methoden zur Messung,
Beobachtung und Verbesserung der Qualität in Open
Data Portalen entwickelt.
Statistisch-Algorithmische, Linked-Data-, sowie
Crowdsourcing-Ansätze werden in einem Data Quality
Monitoring Framework kombiniert.
Auf data.gv.at & opendataportal.at sukzessive evaluiert
und weiterentwickelt.
ADEQUATe: GOALSADEQUATe: Zielsetzung

3. ADEQUATe: GOALS
Improving Data Quality in Open Data
1. Quality measures
2. Evolution monitor
3. Quality improvement through
o Algorithms
o Data linkage (LOD)
o Crowdsourcing & user mechanisms
2 real-world Use Cases
ADEQUATe: GOALS

5. M24
Refinements
M18
Quality improvements Use case connection
M12
Quality monitoring framework Data linkage
M8
Architecture Blueprint
M6
Quality metrics Requirements
ADEQUATe: GOALSADEQUATe: Milestones

6. ADEQUATe: GOALSADEQUATe: Contacts
Martin Kaltenböck
Semantic Web Company (SWC)
m.kaltenboeck@semantic-web.at
Peter Parycek
Donau University Krems
peter.parycek@donau-uni.ac.at
Axel Polleres
Vienna Univ. of Economics and Business
axel.polleres@wu.ac.at
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