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Crowdsourcing

About: Crowdsourcing is a research topic. Over the lifetime, 12889 publications have been published within this topic receiving 230638 citations.


Papers
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Journal ArticleDOI
TL;DR: From experimental results, it can be inferred that the proposed worker-selection incentive mechanism can inspire users to participate in crowd tasks and maximize the utility of mobile crowdsourcing systems effectively.

71 citations

Proceedings ArticleDOI
09 May 2017
TL;DR: A crowd-powered database system CDB is developed that supports crowd-based query optimizations, with focus on join and selection and a unified framework to perform the multi-goal optimization based on the graph model.
Abstract: Crowdsourcing database systems have been proposed to leverage crowd-powered operations to encapsulate the complexities of interacting with the crowd. Existing systems suffer from two major limitations. Firstly, in order to optimize a query, they often adopt the traditional tree model to select an optimized table-level join order. However, the tree model provides a coarse-grained optimization, which generates the same order for different joined tuples and limits the optimization potential that different joined tuples can be optimized by different orders. Secondly, they mainly focus on optimizing the monetary cost. In fact, there are three optimization goals (i.e., smaller monetary cost, lower latency, and higher quality) in crowdsourcing, and it calls for a system to enable multi-goal optimization. To address the limitations, we develop a crowd-powered database system CDB that supports crowd-based query optimizations, with focus on join and selection. CDB has fundamental differences from existing systems. First, CDB employs a graph-based query model that provides more fine-grained query optimization. Second, CDB adopts a unified framework to perform the multi-goal optimization based on the graph model. We have implemented our system and deployed it on AMT, CrowdFlower and ChinaCrowd. We have also created a benchmark for evaluating crowd-powered databases. We have conducted both simulated and real experiments, and the experimental results demonstrate the performance superiority of CDB on cost, latency and quality.

71 citations

Journal ArticleDOI
TL;DR: This article focuses on the question of which ethical questions are raised by data collection with crowdsourcing tools, and identifies fair pay and the related issue of respect for autonomy, as well as problems with the power dynamic between researcher and participant as the major ethical challenges of crowdsourced data.
Abstract: New technologies like large-scale social media sites (e.g., Facebook and Twitter) and crowdsourcing services (e.g., Amazon Mechanical Turk, Crowdflower, Clickworker) are impacting social science research and providing many new and interesting avenues for research. The use of these new technologies for research has not been without challenges, and a recently published psychological study on Facebook has led to a widespread discussion of the ethics of conducting large-scale experiments online. Surprisingly little has been said about the ethics of conducting research using commercial crowdsourcing marketplaces. In this article, I focus on the question of which ethical questions are raised by data collection with crowdsourcing tools. I briefly draw on the implications of Internet research more generally, and then focus on the specific challenges that research with crowdsourcing tools faces. I identify fair pay and the related issue of respect for autonomy, as well as problems with the power dynamic between researcher and participant, which has implications for withdrawal without prejudice, as the major ethical challenges of crowdsourced data. Furthermore, I wish to draw attention to how we can develop a “best practice” for researchers using crowdsourcing tools.

71 citations

Journal ArticleDOI
TL;DR: A passive crowdsourcing channel state information (CSI) based indoor localization scheme, C2IL, built upon an innovative method to accurately estimate the moving speed solely based on 802.11n CSI and designed a trajectory clustering based localization algorithm to provide precise real-time indoor localization and tracking.
Abstract: Numerous indoor localization techniques have been proposed recently to meet the intensive demand for location-based service (LBS). Among them, the most popular solutions are the Wi-Fi fingerprint-based approaches. The core challenge is to lower the cost of fingerprint site-survey. One of the trends is to collect the piecewise data from clients and establish the radio map in crowdsourcing manner. However the low participation rate blocks the practical use. In this work, we propose a passive crowdsourcing channel state information (CSI) based indoor localization scheme, C2IL. Despite a crowdsourcing based approach, our scheme is totally transparent to the client and the only requirement is to connect to our 802.11n access points (APs). C2IL is built upon an innovative method to accurately estimate the moving speed solely based on 802.11n CSI. Knowing the walking speed of a client and its surrounding APs, a graph matching algorithm is employed to extract the received signal strength (RSS) fingerprints and establish the fingerprint map. For localization phase, we design a trajectory clustering based localization algorithm to provide precise real-time indoor localization and tracking. We develop and deploy a practical working system of C2IL in a large office environment. Extensive evaluations indicate that the error of speed estimation is within 3%, and the localization error is within 2 m at 80% time in a very complex indoor environment.

71 citations

Journal ArticleDOI
TL;DR: Crowdsourcing allows acquisition of massive numbers of layperson assessments on an unprecedented scale, and is a convenient, rapid, and reliable means of assessing aesthetic outcome of treatment for unilateral cleft lip.
Abstract: Background:Lack of convenient and reliable methods to grade aesthetic outcomes limits the ability to study results and optimize treatment of unilateral cleft lip. Crowdsourcing methods solicit contributions from a large group to achieve a greater task. The authors hypothesized that crowdsourcing cou

71 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
2023637
20221,420
2021996
20201,250
20191,341
20181,396