Crowdsourced enumeration queries

By Beth Trushkowsky, Tim Kraska, M. Franklin, Purnamrita Sarkar. 2013

120 citations recorded by the source corpus.

Subfields: Computer Science Applications, Management Science and Operations Research, Signal Processing

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Citations in the corpus

A Survey on Data Collection for Machine Learning: A Big Data - AI Integration Perspective (2018, background): “ainty of the collected data. In an open-world assumption, there is no longer a guarantee that all the data can be collected. Instead, one must estimate if enough data was collected. Statistical tools [52] have been proposed for scanning a single table with predicates like “SELECT FLAVORS FROM ICE CR”
Crowdsourced Data Management: Industry and Academic Perspectives (2015, methodology): “One paper that shows how perception-related factors like pop-out can affect result quality is on counting items in a set with a given property [179].”
KATARA: A Data Cleaning System Powered by Knowledge Bases and Crowdsourcing (2015, background): “While approaches have been proposed for open-ended questions to the crowd [38], we leave the problem of extending the structure of the kbs to future work, as discussed in Section 9.”
Query-Oriented Data Cleaning with Oracles (2015, methodology): “The helper method CrowdComplete( 𝑄 ( 𝐷 ) ) (line 7), that poses questions of the type COMPL ( 𝑄 ( 𝐷 )) , needs to know when to stop posting these questions (i.e., when 𝑄 ( 𝐷 ) is complete).”
Corleone: hands-off crowdsourcing for entity matching (2014, background): “Many other works crowdsource joins [19], find the maximal value [11], collect data [28], match schemas [21], and perform data mining [1] and analytics [18].”
Challenges in Data Crowdsourcing (2016, background): “Lastly, we have heard reports of human workers colluding to all provide the same answer for a task [51]: collusion is very hard to detect automatically and correct for (typical algorithms assume independence between workers).”
Crowdsourced Data Management: A Survey (2016, background): “[110] propose a pay-as-you-go approach for crowdsourced enumeration.”
A sample-and-clean framework for fast and accurate query processing on dirty data (2014, background): “Data Cleaning: There have been many studies on various data-cleaning techniques, such as rule-based approaches [17, 23], outlier detection [18,31], filling missing values [48,52], and duplicate detection [9,12].”
A Data Quality Metric (DQM): How to Estimate the Number of Undetected Errors in Data Sets (2016, background): “…of positive votes n + = =1 n + i (this also ensures that n = n + = ∞ i =1 f i ), yielding to the following estimator: Note, that this assumes that each worker is given a randomly selected p pairs/records and that the random samples are not too big to approximate a sample with replacement [30].”
CrowdFill: collecting structured data from the crowd (2014, methodology): “• Finally, we developed CrowdFill as an alternative approach to previously-proposed microtask-based approaches for collecting structured data from the crowd [11, 16, 23].”
Foundations of Crowd Data Sourcing (2015, methodology): “To address this phenomenon, statistical tools are developed in [25], for estimating the future rate of incoming new values based on the rate observed thus far.”
Scaling Up Crowd-Sourcing to Very Large Datasets: A Case for Active Learning (2014, background): “ese systems [17, 19, 24, 25, 33] use optimization techniques to reduce the number of unnecessary questions asked to humans (e.”