InCognitoMatch: Cognitive-aware Matching via Crowdsourcing

Roee Shraga; Coral Scharf; Rakefet Ackerman; Avigdor Gal

doi:10.1145/3318464.3384697

InCognitoMatch: Cognitive-aware Matching via Crowdsourcing

Roee Shraga, Coral Scharf, Rakefet Ackerman, Avigdor Gal

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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.

Original language	English
Title of host publication	SIGMOD 2020 - Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data
Publisher	Association for Computing Machinery
Pages	2753-2756
Number of pages	4
ISBN (Electronic)	9781450367356
DOIs	https://doi.org/10.1145/3318464.3384697
State	Published - 14 Jun 2020
Externally published	Yes
Event	2020 ACM SIGMOD International Conference on Management of Data, SIGMOD 2020 - Portland, United States Duration: 14 Jun 2020 → 19 Jun 2020

Publication series

Name	Proceedings of the ACM SIGMOD International Conference on Management of Data
ISSN (Print)	0730-8078

Conference

Conference	2020 ACM SIGMOD International Conference on Management of Data, SIGMOD 2020
Country/Territory	United States
City	Portland
Period	14/06/20 → 19/06/20

Bibliographical note

Publisher Copyright:
© 2020 Association for Computing Machinery.

Keywords

cognitive-aware
crowdsourcing
data integration
matching

ASJC Scopus subject areas

Software
Information Systems

Access to Document

10.1145/3318464.3384697

Cite this

Shraga, R., Scharf, C., Ackerman, R., & Gal, A. (2020). InCognitoMatch: Cognitive-aware Matching via Crowdsourcing. In SIGMOD 2020 - Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data (pp. 2753-2756). (Proceedings of the ACM SIGMOD International Conference on Management of Data). Association for Computing Machinery. https://doi.org/10.1145/3318464.3384697

@inproceedings{d4ae8b13f037489c9283bcecea4ace1b,

title = "InCognitoMatch: Cognitive-aware Matching via Crowdsourcing",

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.",

keywords = "cognitive-aware, crowdsourcing, data integration, matching",

author = "Roee Shraga and Coral Scharf and Rakefet Ackerman and Avigdor Gal",

note = "Publisher Copyright: {\textcopyright} 2020 Association for Computing Machinery.; 2020 ACM SIGMOD International Conference on Management of Data, SIGMOD 2020 ; Conference date: 14-06-2020 Through 19-06-2020",

year = "2020",

month = jun,

day = "14",

doi = "10.1145/3318464.3384697",

language = "English",

series = "Proceedings of the ACM SIGMOD International Conference on Management of Data",

publisher = "Association for Computing Machinery",

pages = "2753--2756",

booktitle = "SIGMOD 2020 - Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data",

address = "United States",

}

Shraga, R, Scharf, C, Ackerman, R & Gal, A 2020, InCognitoMatch: Cognitive-aware Matching via Crowdsourcing. in SIGMOD 2020 - Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data. Proceedings of the ACM SIGMOD International Conference on Management of Data, Association for Computing Machinery, pp. 2753-2756, 2020 ACM SIGMOD International Conference on Management of Data, SIGMOD 2020, Portland, United States, 14/06/20. https://doi.org/10.1145/3318464.3384697

InCognitoMatch: Cognitive-aware Matching via Crowdsourcing. / Shraga, Roee; Scharf, Coral; Ackerman, Rakefet et al.
SIGMOD 2020 - Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data. Association for Computing Machinery, 2020. p. 2753-2756 (Proceedings of the ACM SIGMOD International Conference on Management of Data).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - InCognitoMatch

T2 - 2020 ACM SIGMOD International Conference on Management of Data, SIGMOD 2020

AU - Shraga, Roee

AU - Scharf, Coral

AU - Ackerman, Rakefet

AU - Gal, Avigdor

PY - 2020/6/14

Y1 - 2020/6/14

N2 - 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.

AB - 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.

KW - cognitive-aware

KW - crowdsourcing

KW - data integration

KW - matching

UR - http://www.scopus.com/inward/record.url?scp=85086248703&partnerID=8YFLogxK

U2 - 10.1145/3318464.3384697

DO - 10.1145/3318464.3384697

M3 - Conference contribution

AN - SCOPUS:85086248703

T3 - Proceedings of the ACM SIGMOD International Conference on Management of Data

SP - 2753

EP - 2756

BT - SIGMOD 2020 - Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data

PB - Association for Computing Machinery

Y2 - 14 June 2020 through 19 June 2020

ER -

InCognitoMatch: Cognitive-aware Matching via Crowdsourcing

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this