CrowdFill: collecting structured data from the crowd

By Hyunjung Park, Jennifer Widom. 2014

66 citations recorded by the source corpus.

Subfields: Computer Science Applications, Computer Vision and Pattern Recognition, Ocean Engineering

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Citations in the corpus

A Survey on Data Collection for Machine Learning: A Big Data - AI Integration Perspective (2018, background): “CrowdFill [53] is a system for collecting parts of”
BoostClean: Automated Error Detection and Repair for Machine Learning (2017, background): “Several tools have been proposed to reduce human burden including automatically generating explanations [50], building more robust interfaces [3,26], and more sophisticated human-inthe-loop processing with crowds [15,21,22,36,40,52].”
Crowdsourced Data Management: Industry and Academic Perspectives (2015, background): “Crowd-Fill [151] addresses a similar problem in a relational setting, where multiple concept types can be crowdsourced.”
Query-Oriented Data Cleaning with Oracles (2015, methodology): “It is a powerful tool that has been employed for database cleaning tasks such as entity/conflict resolution [63, 67], duplicate detection [7, 12], schema matching [69, 37, 45], and filling up missing data [52, 51, 29].”
Wisteria: Nurturing Scalable Data Cleaning Infrastructure (2015, background): “Wisteria allows users to specify declarative data cleaning plans composed of rule-based, learning-based, or crowdbased operators, then iterate rapidly on plans with cost-aware recommendations for improving the accuracy or latency of a plan.”
Crowdsourced Data Management: A Survey (2016, background): “Crowdsourcing can also benefit data management applications , such as data cleaning [93], [119], data integration [58], [73], [127], knowledge construction [9], [12].”
SampleClean: Fast and Reliable Analytics on Dirty Data (2015, background): “While crowdsourcing is an increasingly viable option for correcting some types of errors [29, 22, 48, 24, 38, 4, 14], it comes at the signiﬁcant cost of additional latency and the overhead of managing human workers.”
PrivateClean: Data Cleaning and Differential Privacy (2016, background): “PrivateClean supports cleaning operations that can be represented as deterministic user-deﬁned functions over the discrete attributes that: (1) transform existing attributes, (2) create new attributes, and (3) resolve disparate attribute values to a single canonical representation.”
Cleaning Crowdsourced Labels Using Oracles For Statistical Classification (2018, background): “One category is to leverage humans (either crowd workers or experts) to solve a particular data-cleaning problem, such as entity resolution [9,14,41,43], missing value imputation [30], and data repairing [8,44].”
Foundations of Crowd Data Sourcing (2015, methodology): “, [10, 16, 20, 21]) suggests the construction of declarative frameworks, which outsource the harvesting of certain missing values or missing tuples to the crowd.”