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 …

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

Spatial Crowdsourcing (SC) is a transformative platform that engages individuals, groups
and communities in the act of collecting, analyzing, and disseminating environmental, social …

Micro-task crowdsourcing is rapidly gaining popularity among research communities and
businesses as a means to leverage Human Computation in their daily operations. Unlike …

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 …

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 …

Data quality is one of the most important problems in data management, since dirty data
often leads to inaccurate data analytics results and wrong business decisions. Poor data …

In this paper, we study a hybrid human-machine approach for solving the problem of Entity
Resolution (ER). The goal of ER is to identify all records in a database that refer to the same …