How to Define the Quality of Data? A Feature-Based Literature Survey
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866908297149284352 |
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| author | Matoni, Markus Kesper, Arno Taentzer, Gabriele |
| author_facet | Matoni, Markus Kesper, Arno Taentzer, Gabriele |
| contents | The digital transformation of our society is a constant challenge, as data is generated in almost every digital interaction. To use data effectively, it must be of high quality. This raises the question: what exactly is data quality? A systematic literature review of the existing literature shows that data quality is a multifaceted concept, characterized by a number of quality dimensions. However, the definitions of data quality vary widely. We used feature-oriented domain analysis to specify a taxonomy of data quality definitions and to classify the existing definitions. This allows us to identify research gaps and future topics. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_01491 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | How to Define the Quality of Data? A Feature-Based Literature Survey Matoni, Markus Kesper, Arno Taentzer, Gabriele Databases The digital transformation of our society is a constant challenge, as data is generated in almost every digital interaction. To use data effectively, it must be of high quality. This raises the question: what exactly is data quality? A systematic literature review of the existing literature shows that data quality is a multifaceted concept, characterized by a number of quality dimensions. However, the definitions of data quality vary widely. We used feature-oriented domain analysis to specify a taxonomy of data quality definitions and to classify the existing definitions. This allows us to identify research gaps and future topics. |
| title | How to Define the Quality of Data? A Feature-Based Literature Survey |
| topic | Databases |
| url | https://arxiv.org/abs/2504.01491 |