CrowdScreen: algorithms for filtering data with humans

By Aditya G. Parameswaran, Hector Garcia-Molina, Hyunjung Park, Neoklis Polyzotis, Aditya Ramesh, Jennifer Widom. 2012

250 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Science Applications, Management Science and Operations Research

How researchers see this paper

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

In Artificial Intelligence

In Computer Science Applications

In Management Science and Operations Research

Citations in the corpus

A Survey on Data Collection for Machine Learning: A Big Data - AI Integration Perspective (2018, background): “Get another label [119] and Crowdscreen [120] actively solicit labels while Karger et al.”
Challenges in Data Crowdsourcing (2016, methodology): “In the filtering problem [36], [37], we are given a set of records, and our goal is to identify which records satisfy a given condition or property.”
Artificial Intelligence and Collective Intelligence (2014, background): “(Parameswaran et al. 2010) investigate other budgeted optimization problems and their theoretical properties.”
Comprehensive and reliable crowd assessment algorithms (2014, background): “B) We present experimental results on synthetic data (Section IV-B) and 3 real datasets (Section IV-C) to study the performance of our confidence intervals on k-ary tasks.”
Query-Oriented Data Cleaning with Oracles (2015, methodology): “, using error probability, or an average weighted by trust [50, 49, 56]).”
Truth Inference in Crowdsourcing: Is the Problem Solved? (2017, background): “However, as addressed in [60, 38], a multiple-choice task can be easily transformed to a set of decision-making tasks, e.”
A Framework for Protecting Worker Location Privacy in Spatial Crowdsourcing (2014, background): “, image processing [2], databases [25]) and industry (e.”
Online mobile Micro-Task Allocation in spatial crowdsourcing (2016, methodology): “In recent years, a wide spectrum of fundamental datadriven operations for general crowdsourcing platforms are well studied, such as filtering[13], entity resolution[14], data cleaning[15], topic discovery[16], and a couple of crowdpowered database systems have been successfully developed, such as Cr”
State-of-the-art, challenges, and open issues in the integration of Internet of things and cloud computing (2016, background): “Furthermore, in environments where the user opinion is taken into account, data filtering can be helped in the decision process from users as realized in CrowdScreen [77].”
Crowdsourced Data Management: Industry and Academic Perspectives (2015, methodology): “To accomplish this, we contrast the design choices of two papers on filtering [143] and sorting [132], referring to other papers where it is helpful.”
Crowdsourced Data Management: A Survey (2016, background): “According to whether or not the returned result has some randomness, Zheng et al. [138] classify voting strategies into two categories: deterministic and randomized voting strategies (A similar classification is also proposed in [89]).”
Debiasing social wisdom (2013, background): “The wisdom of the crowd is increasingly finding use in a plethora of contexts ranging from the traditional online surveys to query predicates involving human computation in database applications [20, 19].”
Foundations of Crowd Data Sourcing (2015, methodology): “For example, common tasks to the crowd include classifying single items [6, 19, 23, 24], comparing sets of items [9, 7, 11, 29], and providing missing data items and values [10, 20, 21, 25] in domains where human judgment is required (e.”