Missing data in time series is a pervasive problem that puts obstacles in the way of
advanced analysis. A popular solution is imputation, where the fundamental challenge is to …

Query optimizers rely on accurate cardinality estimates to produce good execution plans.
Despite decades of research, existing cardinality estimators are inaccurate for complex …

Ensuring good data quality in biomedical sciences is crucial for reliable research outcomes,
particularly as precision medicine continues to gain prominence. Missing values …

Data imputation and forecasting are the major research areas in environmental data
engineering. Solving those critical issues has an immense impact on air pollution …

Biobanks that collect deep phenotypic and genomic data across many individuals have
emerged as a key resource in human genetics. However, phenotypes in biobanks are often …

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) …

We introduce Saga, a next-generation knowledge construction and serving platform for
powering knowledge-based applications at industrial scale. Saga follows a hybrid batch …

Given a dataset with incomplete data (eg, missing values), training a machine learning
model over the incomplete data requires two steps. First, it requires a data-effective step that …

This paper explores the utilization of LLMs for data preprocessing (DP), a crucial step in the
data mining pipeline that transforms raw data into a clean format. We instruction-tune local …

Organizations are increasingly relying on data to support decisions. When data contains
private and sensitive information, the data owner often desires to publish a synthetic …