Raha: A Configuration-Free Error Detection System
Name
raha.pdf
Description
Accepted version
Size
1.72 MB
Format
Adobe PDF
Checksum (MD5)
70189a4b143314d75f6123ea90e8dbf2
Author(s)
Mahdavi, Mohammad
Abedjan, Ziawasch
Castro Fernandez, Raul
Madden, Samuel
Ouzzani, Mourad
Stonebraker, Michael
Tang, Nan
Date Issued
2019
Journal
Proceedings of the ACM SIGMOD International Conference on Management of Data
Publisher
Association for Computing Machinery (ACM)
Citation
Mahdavi, Mohammad, Abedjan, Ziawasch, Castro Fernandez, Raul, Madden, Samuel, Ouzzani, Mourad et al. 2019. "Raha: A Configuration-Free Error Detection System." Proceedings of the ACM SIGMOD International Conference on Management of Data.
Version
Author's final manuscript
Abstract
© 2019 Association for Computing Machinery. Detecting erroneous values is a key step in data cleaning. Error detection algorithms usually require a user to provide input configurations in the form of rules or statistical parameters. However, providing a complete, yet correct, set of configurations for each new dataset is not trivial, as the user has to know about both the dataset and the error detection algorithms upfront. 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 various types of data errors, we can generate an expressive feature vector for each tuple value. 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.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1145/3299869.3324956
