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

Finding joinable tables in data lakes is key procedure in many applications such as data
integration, data augmentation, data analysis, and data market. Traditional approaches that …

The recent Programming By Example (PBE) techniques such as FlashFill have shown great
promise for enabling end-users to perform data transformation tasks using input-output …

Due to the usefulness in data enrichment for data analysis tasks, joinable table discovery
has become an important operation in data lake management. Existing approaches target …

Modern dual in-line memory modules (DIMMs) support processing-in-memory (PIM) by
implementing in-DIMM processors (IDPs) located near memory banks. PIM can greatly …

Data errors are ubiquitous in tables. Extensive research in this area has resulted in a rich
variety of techniques, each often targeting a specific type of errors, eg, numeric outliers …

Traditional equi-join relies solely on string equality comparisons to perform joins. However,
in scenarios such as ad-hoc data analysis in spreadsheets, users increasingly need to join …

Fuzzy similarity join is an important database operator widely used in practice. So far the
research community has focused exclusively on optimizing fuzzy joinscalability. However …

Data Transformation is a long-standing problem in data management. Recent work adopts
a" transform-by-example"(TBE) paradigm to infer transformation programs based on user …

Given a single column of values, existing approaches typically employ regex-like rules to
detect errors by finding anomalous values inconsistent with others. Such techniques make …