Ontological Deep Data Cleaning
| Autor: | Spencer Seeger, David W. Embley, Scott N. Woodfield, Brenden Grace, Stephen W. Liddle, Samuel Litster |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Computer science
010401 analytical chemistry Value (computer science) 020207 software engineering 02 engineering and technology Investigative journalism Field tests Resolution (logic) computer.software_genre 01 natural sciences Fuzzy logic 0104 chemical sciences Data quality 0202 electrical engineering electronic engineering information engineering Data mining computer |
| Zdroj: | Conceptual Modeling ISBN: 9783030008468 ER |
| DOI: | 10.1007/978-3-030-00847-5_9 |
| Popis: | Analytical applications such as forensics, investigative journalism, and genealogy require deep data cleaning in which application-dependent semantic errors and inconsistencies are detected and resolved. To facilitate deep data cleaning, the application is modeled ontologically, and real-world crisp and fuzzy constraints are specified. Conceptual-model-based declarative specification enables rapid development and modification of the usually large number of constraints. Field tests show the prototype’s ability to detect errors and either resolve them or provide guidance for user-involved resolution. A user study also shows the value of declarative specification in deep data cleaning applications. |
| Databáze: | OpenAIRE |
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