Crowdsourced Data Management: Industry and Academic Perspectives

By Adam Marcus, Aditya G. Parameswaran. 2015

87 citations recorded by the source corpus.

Subfields: Management Information Systems

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Management Information Systems

1. Sanjay Krishnan
2. Eugene Wu
3. Tim Kraska
4. Yeounoh Chung
5. Marc-André Zöller
6. Marco F. Huber

Citations in the corpus

• A Survey on Data Collection for Machine Learning: A Big Data - AI Integration Perspective (2018, background): “a augmentation if there is existing data where some missing parts needs to be filled in. 2.3.1 Crowdsourcing Crowdsourcing is used to solve a wide range of problems, and there are many surveys as well [57]–[60]. One of the earliest and most popular platforms is Amazon Mechanical Turk [61] where tasks”
• BoostClean: Automated Error Detection and Repair for Machine Learning (2017, background): “Such labels may be collected as part of a gold standard dataset [36] or by cross-referencing the data with other sources [34].”
• Data Cleaning: Overview and Emerging Challenges (2016, background): “Active Learning in Crowdsourcing: Crowdsourcing is widely applied in industry for data cleaning [56].”
• A Data-Driven Analysis of Workers' Earnings on Amazon Mechanical Turk (2017, background): “that is widely used by industry [32,46] and the HCI community, as well as by other research areas such as NLP and computer vision [15,43].”
• A Data Quality Metric (DQM): How to Estimate the Number of Undetected Errors in Data Sets (2016, methodology): “The focus of this paper is on entity resolution, though we will show how our techniques can also be applied to other types of data errors (e.g., missing or wrong values).”
• Challenges in Data Crowdsourcing (2016, background): “many marketplaces in existence, and further, there are many marketplaces that are internal to organizations [31].”
• A Glimpse Far into the Future: Understanding Long-term Crowd Worker Accuracy (2016, background): “These communities use crowd work to complete hundreds of thousands of tasks per day [37], from which new datasets with over 20 million annotations can be produced within a few months [25].”