Detecting and repairing dirty data is one of the perennial challenges in data analytics, and
failure to do so can result in inaccurate analytics and unreliable decisions. Over the past few …

Massive amounts of data are available for the organization which will influence their
business decision. Data collected from the various resources are dirty and this will affect the …

Foundation Models (FMs) are models trained on large corpora of data that, at very large
scale, can generalize to new tasks without any task-specific finetuning. As these models …

Deep learning (DL) techniques have obtained remarkable achievements on various tasks,
such as image recognition, object detection, and language modeling. However, building a …

Abstract Machine learning (ML) has become a vital part in many aspects of our daily life.
However, building well performing machine learning applications requires highly …

This is an overview of the end-to-end data cleaning process. Data quality is one of the most
important problems in data management, since dirty data often leads to inaccurate data …

We introduce HoloClean, a framework for holistic data repairing driven by probabilistic
inference. HoloClean unifies existing qualitative data repairing approaches, which rely on …

Data cleaning has played a critical role in ensuring data quality for enterprise applications.
Naturally, there has been extensive research in this area, and many data cleaning …

This paper presents the design of a read-optimized relational DBMS that contrasts sharply
with most current systems, which are write-optimized. Among the many differences in its …

We introduce a few-shot learning framework for error detection. We show that data
augmentation (a form of weak supervision) is key to training high-quality, ML-based error …