One of the most critical tasks for improving data quality and increasing the reliability of data
analytics is Entity Resolution (ER), which aims to identify different descriptions that refer to …

Detecting and repairing dirty data is one of the perennial challenges in data analytics, and
failure to do so can result in inaccurate analytics and unreliable decisions. Over the past few …

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

Entity matching is the problem of identifying which records refer to the same real-world
entity. It has been actively researched for decades, and a variety of different approaches …

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

Recent approaches to crowdsourcing entity matching (EM) are limited in that they
crowdsource only parts of the EM workflow, requiring a developer to execute the remaining …

Crowdsourcing is widely accepted as a means for resolving tasks that machines are not
good at. Unfortunately, Crowdsourcing may yield relatively low-quality results if there is no …

A crowdsourcing system, such as the Amazon Mechanical Turk (AMT), provides a platform
for a large number of questions to be answered by Internet workers. Such systems have …

Since Jeff Howe introduced the term Crowdsourcing in 2006, this human-powered problem-
solving paradigm has gained a lot of attention and has been a hot research topic in the field …

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