2 citations · 3 across the 5 of their papers we have counts for
4 papers · 1 filter
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…
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…
Extracting JSON Schemas with Tagged Unions
Stefan Klessinger, Meike Klettke, Uta Störl +1
With data lakes and schema-free NoSQL document stores, extracting a descriptive schema from JSON data collections is an acute challenge. In this paper, we target the discovery of t…
NoSQL Database Tuning through Machine Learning
Florian Eppinger, Uta Störl
NoSQL databases have become an important component of many big data and real-time web applications. Their distributed nature and scalability make them an ideal data storage reposit…