Abstract Machine Learning (ML) is integrated into a growing number of systems for various
applications. Because the performance of an ML model is highly dependent on the quality of …

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 …

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 …

Query optimization is one of the most challenging problems in database systems. Despite
the progress made over the past decades, query optimizers remain extremely complex …

Data quality affects machine learning (ML) model performances, and data scientists spend
considerable amount of time on data cleaning before model training. However, to date, there …

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) …

Traditional error correction solutions leverage handmaid rules or master data to find the
correct values. Both are often amiss in real-world scenarios. Therefore, it is desirable to …

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 …

Deep Learning revolutionizes almost all fields of computer science including data
management. However, the demand for high-quality training data is slowing down deep …

Data preparation is widely recognized as the most time-consuming process in modern
business intelligence (BI) and machine learning (ML) projects. Automating complex data …