Keywords
data quality, data cleaning, declarative constraint specification, conceptual-model-based deep data cleaning
Abstract
Analytical applications such as forensics, investigative journalism, and genealogy require deep data cleaning in which applicationdependent 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. Conceptualmodel- 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.
Original Publication Citation
"Ontological Deep Data Cleaning", International Conference on Conceptual Modeling (ER 2018), Pages pp. 100-108, 2018
BYU ScholarsArchive Citation
Woodfield, Scott N.; Seeger, Spencer; Litster, Samuel; Liddle, Stephen W.; Grace, Brenden; and Embley, David W., "Ontological Deep Data Cleaning" (2018). Faculty Publications. 9507.
https://scholarsarchive.byu.edu/facpub/9507
Document Type
Conference Paper
Publication Date
2018
Publisher
International Conference on Conceptual Modeling
Language
English
College
Marriott School of Business
Department
Information Systems Management
Copyright Status
© Springer Nature Switzerland AG 2018
Copyright Use Information
http://lib.byu.edu/about/copyright/