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Active Learning for Data Quality Control: A Survey
Data quality plays a vital role in scientific research and decision-making across industries. Thus, it is crucial to incorporate the data quality control (DQC) process, which comprises various actions and operations to detect and correct data errors. The ...
BUNNI: Learning Repair Actions in Rule-driven Data Cleaning
In this work, we address the challenging and open problem of involving non-expert users in the data repairing problem as first-class citizens. Despite a large number of proposals that have been devoted to cleaning data from the point of view of expert ...
Data Validation Utilizing Expert Knowledge and Shape Constraints
Data validation is a primary concern in any data-driven application, as undetected data errors may negatively affect machine learning models and lead to suboptimal decisions. Data quality issues are usually detected manually by experts, which becomes ...
Thinking in Categories: A Survey on Assessing the Quality for Time Series Synthesis
- Michael Stenger,
- André Bauer,
- Thomas Prantl,
- Robert Leppich,
- Nathaniel Hudson,
- Kyle Chard,
- Ian Foster,
- Samuel Kounev
Time series data are widely used and provide a wealth of information for countless applications. However, some applications are faced with a limited amount of data, or the data cannot be used due to confidentiality concerns. To overcome these obstacles, ...
