One of the most critical tasks for improving data quality and increasing the reliability of data
analytics is Entity Resolution (ER), which aims to identify different descriptions that refer to …

There is a trend that, virtually everyone, ranging from big Web companies to traditional
enterprisers to physical science researchers to social scientists, is either already …

Entity matching (EM) finds data instances that refer to the same real-world entity. In this
paper we examine applying deep learning (DL) to EM, to understand DL's benefits and …

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 …

Profiling data to determine metadata about a given dataset is an important and frequent
activity of any IT professional and researcher and is necessary for various use-cases. It …

Data cleaning is an important problem and data quality rules are the most promising way to
face it with a declarative approach. Previous work has focused on specific formalisms, such …

Despite the increasing importance of data quality and the rich theoretical and practical
contributions in all aspects of data cleaning, there is no single end-to-end off-the-shelf …

Big Data now pervades every sector and function of the global economy. This paper focuses
on the gaps between Big Data and the current capabilities of data analysis in continuous …

Entity resolution (ER) is a significant task in data integration, which aims to detect all entity
profiles that correspond to the same real-world entity. Due to its inherently quadratic …