Data analysis often uses data sets that were collected for different purposes. Indeed, new
insights are often obtained by combining data sets that were produced independently of …

Detecting erroneous values is a key step in data cleaning. Error detection algorithms usually
require a user to provide input configurations in the form of rules or statistical parameters …

Can AI help automate human-easy but computer-hard data preparation tasks that burden
data scientists, practitioners, and crowd workers? We answer this question by presenting …

Data analysis impacts virtually every aspect of our society today. Often, this analysis is
performed on an existing dataset, possibly collected through a process that the data …

With Knowledge Graphs (KGs) at the center of numerous applications such as recommender
systems and question-answering, the need for generalized pipelines to construct and …

With knowledge graphs (KGs) at the center of numerous applications such as recommender
systems and question answering, the need for generalized pipelines to construct and …

Generative AI has made significant strides, yet concerns about the accuracy and reliability of
its outputs continue to grow. Such inaccuracies can have serious consequences such as …

Isolate data among different campus information systems and not much effective information
among the big data generated by these systems cause that it is a challenge for predicting …

Data cleaning (or data repairing) is considered a crucial problem in many database-related
tasks. It consists in making a database consistent with respect to a given set of constraints. In …

The analyst effort in data cleaning is gradually shifting away from the design of hand-written
scripts to building and tuning complex pipelines of automated data cleaning libraries. Hyper …