Alireza Heidari, Ihab Ilyas and international colleagues develop new AI tool able to better identify bad data

Tuesday, April 23, 2019

PhD student Alireza Heidari and Professor Ihab Ilyas at the Cheriton School of Computer Science along with international colleagues have developed a novel tool to manage the quality of your data. Called HoloClean, this revolutionary tool is the first to use artificial intelligence to sift out dirty data and correct errors before processing it.

“More and more machines are making decisions for us, so all our lives are touched by dirty data daily,” said Professor Ilyas. “If organizations like banks or utility companies are working with bad data, it could negatively impact things such as credit scores or mortgage approvals.”

The HoloClean system, developed by Professor Ilyas and his colleagues from the University of Wisconsin and Stanford University, successfully tackles the problem of insufficient training data by automatically generating bad examples without messing with the information, but enough to train the system to find errors and correct them on its own. 

After the AI is trained, it can then figure out independently what’s an error, what’s not, and if there’s an error determine the most probable value for the missing data. Users will then have a cleaner dataset to use in their analytics, which will produce more trustworthy results.  

“This work deviates from the old way of manually trying to clean the data, which was expensive, didn’t scale, and does not meet the current needs for cleaning the data,” said Professor Ilyas.

“This system addresses the problem where the information is out there, and people are using it to run analytics, but it is not correct. It doesn’t provide information that was not there, but instead corrects information you assume is correct.”

The next step for the researchers is to pair error detection and data repair in one end-to-end solution for the ultimate data quality dashboard.


The paper detailing the error-detection module titled “HoloDetect: Few-shot Learning for Error Detection” is slated to appear in Proceedings of the 2019 ACM SIGMOD conference. It is authored by Professor Ihab Ilyas, Cheriton School of Computer Science PhD candidate, Alireza Heidari and their colleagues — Theodoros Rekatsinas, assistant professor in the Department of Computer Sciences at the University of Wisconsin-Madison and Joshua McGrath, academic researcher at the University of Wisconsin.

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