Many data management and analytics tasks, such as entity resolution, cannot be solely
addressed by automated processes. Crowdsourcing is an effective way to harness the …

Machine learning (ML) has widespread applications and has revolutionized many
industries, but suffers from several challenges. First, sufficient high-quality training data is …

The lack of sufficient labeled data is a key bottleneck for practitioners in many real-world
supervised machine learning (ML) tasks. In this paper, we study a new problem, namely …

Entity resolution (ER) is a core problem of data integration. The state-of-the-art (SOTA)
results on ER are achieved by deep learning (DL) based methods, trained with a lot of …

Sufficient good features are indispensable to train well-performed machine learning models.
However, it is com-mon that good features are not always enough, where feature …

With the rapid development of smartphones, spatial crowdsourcing platforms are getting
popular. A foundational research of spatial crowdsourcing is to allocate micro-tasks to …

Outlier detection is critical to a large number of applications from finance fraud detection to
health care. Although numerous approaches have been proposed to automatically detect …

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 …

Recently, crowdsourcing has emerged as a new computing paradigm to solve problems that
need human intrinsic, such as image annotation. However, there are two limitations in …

Data exploration—the problem of extracting knowledge from database even if we do not
know exactly what we are looking for—is important for data discovery and analysis …