The last few years witnessed significant advances in building automated or semi-automated
data quality, data cleaning and data integration systems powered by machine learning (ML) …

The problem of mining integrity constraints from data has been extensively studied over the
past two decades for commonly used types of constraints including the classic Functional …

Record fusion is the task of aggregating multiple records that correspond to the same real-
world entity in a database. We can view record fusion as a machine learning problem where …

We introduce a learning framework for the problem of unifying conflicting data in multiple
records referring to the same entity—we call this problem “record fusion.” Record fusion …

5 Conclusion To conclude, we suggest opening a new chapter of data quality and data
cleaning that understands the entire data processing pipeline, in particular tracing it to the …

In most theoretical studies on missing data analysis, data is typically assumed to be missing
according to a specific probabilistic model. However, such assumption may not accurately …

Data deduplication is the task of detecting records in a database that correspond to the
same real-world entity. Our goal is to develop a procedure that samples uniformly from the …

Many errors cannot be detected or repaired without taking into account the underlying
structure and dependencies in the dataset. One way of modeling the structure of the data is …

Inference in structured prediction is naturally modeled with a graph, where the goal is to
recover the unknown true label for each node given noisy observations corresponding to …