Many important data management and analytics tasks cannot be completely addressed by
automated processes. Crowdsourcing is an effective way to harness human cognitive …

Crowdsourcing has emerged as a novel problem-solving paradigm, which facilitates
addressing problems that are hard for computers, eg, entity resolution and sentiment …

Training data creation is increasingly a key bottleneck for develo** machine learning,
especially for deep learning systems. Active learning provides a cost-effective means for …

Any important data management and analytics tasks cannot be completely addressed by
automated processes. These tasks, such as entity resolution, sentiment analysis, and image …

Entity resolution is central to data integration and data cleaning. Algorithmic approaches
have been improving in quality, but remain far from perfect. Crowdsourcing platforms offer a …

Dataframes are a popular abstraction to represent, prepare, and analyze data. Despite the
remarkable success of dataframe libraries in Rand Python, dataframes face performance …

The development of crowdsourced query processing systems has recently attracted a
significant attention in the database community. A variety of crowdsourced queries have …

Many works have applied crowdsourcing to entity matching (EM). While promising, these
approaches are limited in that they often require a developer to be in the loop. As such, it is …

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 …

Large language models (LLMs) are incredibly powerful at comprehending and generating
data in the form of text, but are brittle and error-prone. There has been an advent of toolkits …