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InCognitoMatch: Cognitive-aware Matching via Crowdsourcing

Authors:

Roee Shraga,

Coral Scharf,

Rakefet Ackerman,

Avigdor GalAuthors Info & Claims

SIGMOD '20: Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data

Pages 2753 - 2756

https://doi.org/10.1145/3318464.3384697

Published: 31 May 2020 Publication History

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Abstract

We present InCognitoMatch, the first cognitive-aware crowdsourcing application for matching tasks. InCognitoMatch provides a handy tool to validate, annotate, and correct correspondences using the crowd whilst accounting for human matching biases. In addition, InCognitoMatch enables system administrators to control context information visible for workers and analyze their performance accordingly. For crowd workers, InCognitoMatch is an easy-to-use application that may be accessed from multiple crowdsourcing platforms. In addition, workers completing a task are offered suggestions for followup sessions according to their performance in the current session. For this demo, the audience will be able to experience InCognitoMatch thorough three use-cases, interacting with system as workers and as administrators.

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Cited By

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Solomon MGenossar BGal AFekete JOmidvar-Tehrani BRong KShraga R(2024)CopycHats: Question Sequencing with Artificial AgentsProceedings of the 2024 Workshop on Human-In-the-Loop Data Analytics10.1145/3665939.3665963(1-7)Online publication date: 14-Jun-2024
https://dl.acm.org/doi/10.1145/3665939.3665963
Chen YZhang SZhou ZWang XLiang YChen NYan YXiao MWu JQian ZXu H(2024)MACRO: Incentivizing Multi-Leader Game-Based Pareto-Efficient Crowdsourcing for Video Analytics2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00029(289-302)Online publication date: 13-May-2024
https://doi.org/10.1109/ICDE60146.2024.00029
Paulino DGuimarães DCorreia ARibeiro JBarroso JParedes H(2023)A Model for Cognitive Personalization of Microtask DesignSensors10.3390/s2307357123:7(3571)Online publication date: 29-Mar-2023
https://doi.org/10.3390/s23073571
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Index Terms

InCognitoMatch: Cognitive-aware Matching via Crowdsourcing
1. Information systems
  1. Data management systems
    1. Information integration
      1. Mediators and data integration
  2. World Wide Web
    1. Web applications
      1. Crowdsourcing

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Information & Contributors

Information

Published In

SIGMOD '20: Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data

June 2020

2925 pages

ISBN:9781450367356

DOI:10.1145/3318464

General Chairs:
David Maier
Portland State University, USA
,
Rachel Pottinger
University of British Columbia, Canada
,
Program Chairs:
AnHai Doan
University of Wisconsin, USA
,
Wang-Chiew Tan
Megagon Labs, USA
,
Publications Chairs:
Abdussalam Alawini
University of Illinois at Urbana-Champaign, USA
,
Hung Q. Ngo
RelationalAI, USA

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 31 May 2020

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SIGMOD/PODS '20

Sponsor:

SIGMOD

SIGMOD/PODS '20: International Conference on Management of Data

June 14 - 19, 2020

OR, Portland, USA

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Overall Acceptance Rate 785 of 4,003 submissions, 20%

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Cited By

View all

Solomon MGenossar BGal AFekete JOmidvar-Tehrani BRong KShraga R(2024)CopycHats: Question Sequencing with Artificial AgentsProceedings of the 2024 Workshop on Human-In-the-Loop Data Analytics10.1145/3665939.3665963(1-7)Online publication date: 14-Jun-2024
https://dl.acm.org/doi/10.1145/3665939.3665963
Chen YZhang SZhou ZWang XLiang YChen NYan YXiao MWu JQian ZXu H(2024)MACRO: Incentivizing Multi-Leader Game-Based Pareto-Efficient Crowdsourcing for Video Analytics2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00029(289-302)Online publication date: 13-May-2024
https://doi.org/10.1109/ICDE60146.2024.00029
Paulino DGuimarães DCorreia ARibeiro JBarroso JParedes H(2023)A Model for Cognitive Personalization of Microtask DesignSensors10.3390/s2307357123:7(3571)Online publication date: 29-Mar-2023
https://doi.org/10.3390/s23073571
Shraga R(2022)HumanALProceedings of the Workshop on Human-In-the-Loop Data Analytics10.1145/3546930.3547496(1-8)Online publication date: 12-Jun-2022
https://dl.acm.org/doi/10.1145/3546930.3547496
Paulino DCorreia AGuimaraes DBarroso JParedes H(2022)Uncovering the Potential of Cognitive Personalization for UI Adaptation in Crowd Work2022 IEEE 25th International Conference on Computer Supported Cooperative Work in Design (CSCWD)10.1109/CSCWD54268.2022.9776164(484-489)Online publication date: 4-May-2022
https://doi.org/10.1109/CSCWD54268.2022.9776164
Ardalan APaulsen DSaini ACai WDoan A(2022)Toward Data Cleaning with a Target Accuracy: A Case Study for Value Normalization2022 IEEE International Conference on Big Data (Big Data)10.1109/BigData55660.2022.10020821(3975-3981)Online publication date: 17-Dec-2022
https://doi.org/10.1109/BigData55660.2022.10020821
Phan TQuach NNguyen TNguyen TJo JNguyen Q(2022)Real-time wildfire detection with semantic explanationsExpert Systems with Applications10.1016/j.eswa.2022.117007201(117007)Online publication date: Sep-2022
https://doi.org/10.1016/j.eswa.2022.117007
Shraga RAmir OGal A(2021)Learning to Characterize Matching Experts2021 IEEE 37th International Conference on Data Engineering (ICDE)10.1109/ICDE51399.2021.00111(1236-1247)Online publication date: Apr-2021
https://doi.org/10.1109/ICDE51399.2021.00111

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