Holoclean
Noisy and erroneous data is a major bottleneck in analytics. Data cleaning and repairing account for about 60% of the work of data scientists. To address this bottleneck, we recently introduced HoloClean, a semi-automated data repairing framework that relies on statistical learning and inference to repair errors in structured data. In HoloClean, we build upon the paradigm of weak supervision and demonstrate how to leverage diverse signals, including user-defined heuristic rules (such as generalized data integrity constraints) to repair erroneous data.
Holoclean object
| class HoloClean()
class Session("Session", holo_obj)
|