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Paper Accepted for SIGMOD 2019
by Mohammad Mahdavi, Ziawasch Abedjan, Raul Castro Fernandez, Samuel Madden, Mourad Ouzzani, Michael Stonebraker, and Nan Tang was accepted for SIGMOD 2019. This was a joint project between TU Berlin, MIT, and QCRI.
Here is the abstract:
In this paper, we present Raha, a new configuration-free error detection system. By generating a limited number of configurations for error detection algorithms that cover types of data errors, we can generate an expressive feature vector for each tuple value in the dataset. Leveraging these feature vectors, we propose a novel sampling and classification scheme that effectively chooses the most representative values for training. Furthermore, our system can exploit historical data to filter out irrelevant error detection algorithms and configurations.
In our experiments, Raha outperforms the state-of-the-art error detection techniques with no more than 20 labeled tuples on each dataset."