3 papers
cs.DB2026
Rethinking Accuracy: A Weighted Error-Based Metric for Data Quality
Valerie Restat, Uta Störl
Real data often contains errors, which is why data engineers spend a lot of time creating data cleaning pipelines to ensure the best possible data quality. However, it is often dif…
cs.DB2026
Extending GouDa: Generation of Universal Datasets with (and without) Errors for Data Quality Benchmarking
Valerie Restat, André Conrad, Kevin M. Kramer +1
Synthetic data is extremely important in areas such as data quality, data cleaning, and machine learning. It enables the analysis of use cases in which real data is insufficient, u…
cs.ET2024
Hybrid Data Management Architecture for Present Quantum Computing
Markus Zajac, Uta Störl
Quantum computers promise polynomial or exponential speed-up in solving certain problems compared to classical computers. However, in practical use, there are currently a number of…