Abstract
Accuracy, among the most discussed data quality dimensions in literature, reflects the extent to which data values match a baseline perceived to be correct – e.g., the true real-world attribute values, or another validated dataset. Even when data values are accurate when acquired, their accuracy may degrade over time - certain properties of real-world entities may change, while the data values that reflect them are not being updated. Drawing on that assumption, this study suggests a Markov-Chain model that describes accuracy degradation over time – this by assessing the likelihood of a data attribute to transition from one state to another within a given time period. Evaluation of the model with real-world data shows its potential contribution for a few key data-quality management tasks, such as the prediction of accuracy degradation, and the development of data auditing and maintenance policies.
Original language | English |
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Title of host publication | Proceedings of ICIQ 2012 |
Subtitle of host publication | 17th International Conference on Information Quality |
Editors | Laure Berti-Equille, Isabelle Comyn-Wattiau, Monica Scannapieco |
Publisher | MIT |
Pages | 99-110 |
Number of pages | 12 |
ISBN (Electronic) | 9781627483964 |
State | Published - 1 Jan 2012 |
Event | 17th International Conference on Information Quality, ICIQ 2012 - Paris, France Duration: 16 Nov 2012 → 17 Nov 2012 |
Conference
Conference | 17th International Conference on Information Quality, ICIQ 2012 |
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Country/Territory | France |
City | Paris |
Period | 16/11/12 → 17/11/12 |
Keywords
- Accuracy
- Currency
- Data Quality
- Markov-Chain Model
ASJC Scopus subject areas
- Safety, Risk, Reliability and Quality
- Information Systems