Entity Resolution (ER), a core task of Data Integration, detects different entity profiles that
correspond to the same real-world object. Due to its inherently quadratic complexity, a series …

Entity matching refers to the task of determining whether two different representations refer
to the same real-world entity. It continues to be a prevalent problem for many organizations …

Dataset discovery from data lakes is essential in many real application scenarios. In this
paper, we propose Starmie, an end-to-end framework for dataset discovery from data lakes …

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 requirement for appropriate ways to measure the similarity between data objects is a
common but vital task in various domains, such as data mining, machine learning and so on …

Real-world applications frequently seek to solve a general form of the Entity Matching (EM)
problem to find associated entities. Such scenarios include matching jobs to candidates in …

How can we discover join relationships among columns of tabular data in a data repository?
Can this be done effectively when metadata is missing? Traditional column matching works …

The healthcare industry faces serious problems with health data. Firstly, health data is
fragmented and its quality needs to be improved. Data fragmentation means that it is difficult …

Set similarity search is a fundamental operation in a variety of applications. While many
previous studies focus on threshold based set similarity search and join, few efforts have …

Dictionary-based entity extraction is an important task in many data analysis applications,
such as academic search, document classification, and code auto-debugging. To improve …