Clean Me If You Can: A Large Collection of Real-World Addresses for Data Cleaning Benchmarking
Fatemeh Ahmadi
Tobias Bernhard
Mohamed Abdelmaksoud
Luca Zecchini
Tilmann Rabl
Ziawasch Abedjan
There has been extensive research on automating and scaling data cleaning, i.e., the detection and correction of erroneous values in tabular data. Yet, existing approaches often perform well only within controlled environments. One of the major bottlenecks in data cleaning research is the lack of real-world datasets. In this paper, we address this gap by providing a large, dirty dataset with postal entries and their corresponding ground truth. We discuss the design decisions and challenges for obtaining the dataset. We demonstrate the limitations of existing cleaning approaches when faced with our proposed datasets and derive guidelines for future research.
