Worker skill estimation in team-based tasks

Habibur Rahman; Saravanan Thirumuruganathan; Senjuti Basu Roy; Sihem Amer-Yahia; Gautam Das

doi:10.14778/2809974.2809977

Worker skill estimation in team-based tasks

Habibur Rahman, Saravanan Thirumuruganathan, Senjuti Basu Roy, Sihem Amer-Yahia, Gautam Das

Research output: Chapter in Book/Report/Conference proceeding › Chapter

31 Scopus citations

Abstract

Many emerging applications such as collaborative editing, multi-player games, or fan-subbing require to form a team of experts to accomplish a task together. Existing research has investigated how to assign workers to such team-based tasks to ensure the best outcome assuming the skills of individual workers to be known. In this work, we investigate how to estimate individual worker's skill based on the outcome of the team-based tasks they have undertaken. We consider two popular skill aggregation functions and estimate the skill of the workers, where skill is either a deterministic value or a probability distribution. We propose effcient solutions for worker skill estimation using continuous and discrete optimization techniques. We present comprehensive experiments and validate the scalability and effectiveness of our proposed solutions using multiple real-world datasets.

Original language	English (US)
Title of host publication	Proceedings of the VLDB Endowment
Editors	Christophe Claramunt, Simonas Saltenis, Ki-Joune Li
Publisher	Association for Computing Machinery
Pages	1142-1153
Number of pages	12
Volume	8
Edition	11 11
DOIs	https://doi.org/10.14778/2809974.2809977
State	Published - 2015
Externally published	Yes
Event	3rd Workshop on Spatio-Temporal Database Management, STDBM 2006, Co-located with the 32nd International Conference on Very Large Data Bases, VLDB 2006 - Seoul, Korea, Republic of Duration: Sep 11 2006 → Sep 11 2006

Other

Other	3rd Workshop on Spatio-Temporal Database Management, STDBM 2006, Co-located with the 32nd International Conference on Very Large Data Bases, VLDB 2006
Country/Territory	Korea, Republic of
City	Seoul
Period	9/11/06 → 9/11/06

All Science Journal Classification (ASJC) codes

Computer Science (miscellaneous)
General Computer Science

Access to Document

10.14778/2809974.2809977

Cite this

@inbook{8134313edc0f4892aec153590141e5d5,

title = "Worker skill estimation in team-based tasks",

abstract = "Many emerging applications such as collaborative editing, multi-player games, or fan-subbing require to form a team of experts to accomplish a task together. Existing research has investigated how to assign workers to such team-based tasks to ensure the best outcome assuming the skills of individual workers to be known. In this work, we investigate how to estimate individual worker's skill based on the outcome of the team-based tasks they have undertaken. We consider two popular skill aggregation functions and estimate the skill of the workers, where skill is either a deterministic value or a probability distribution. We propose effcient solutions for worker skill estimation using continuous and discrete optimization techniques. We present comprehensive experiments and validate the scalability and effectiveness of our proposed solutions using multiple real-world datasets.",

author = "Habibur Rahman and Saravanan Thirumuruganathan and Roy, {Senjuti Basu} and Sihem Amer-Yahia and Gautam Das",

note = "Publisher Copyright: {\textcopyright} 2015 VLDB Endowment 2150-8097/15/07; 3rd Workshop on Spatio-Temporal Database Management, STDBM 2006, Co-located with the 32nd International Conference on Very Large Data Bases, VLDB 2006 ; Conference date: 11-09-2006 Through 11-09-2006",

year = "2015",

doi = "10.14778/2809974.2809977",

language = "English (US)",

volume = "8",

pages = "1142--1153",

editor = "Christophe Claramunt and Simonas Saltenis and Ki-Joune Li",

booktitle = "Proceedings of the VLDB Endowment",

publisher = "Association for Computing Machinery",

edition = "11 11",

}

Rahman, H, Thirumuruganathan, S, Roy, SB, Amer-Yahia, S & Das, G 2015, Worker skill estimation in team-based tasks. in C Claramunt, S Saltenis & K-J Li (eds), Proceedings of the VLDB Endowment. 11 11 edn, vol. 8, Association for Computing Machinery, pp. 1142-1153, 3rd Workshop on Spatio-Temporal Database Management, STDBM 2006, Co-located with the 32nd International Conference on Very Large Data Bases, VLDB 2006, Seoul, Korea, Republic of, 9/11/06. https://doi.org/10.14778/2809974.2809977

TY - CHAP

T1 - Worker skill estimation in team-based tasks

AU - Rahman, Habibur

AU - Thirumuruganathan, Saravanan

AU - Roy, Senjuti Basu

AU - Amer-Yahia, Sihem

AU - Das, Gautam

PY - 2015

Y1 - 2015

N2 - Many emerging applications such as collaborative editing, multi-player games, or fan-subbing require to form a team of experts to accomplish a task together. Existing research has investigated how to assign workers to such team-based tasks to ensure the best outcome assuming the skills of individual workers to be known. In this work, we investigate how to estimate individual worker's skill based on the outcome of the team-based tasks they have undertaken. We consider two popular skill aggregation functions and estimate the skill of the workers, where skill is either a deterministic value or a probability distribution. We propose effcient solutions for worker skill estimation using continuous and discrete optimization techniques. We present comprehensive experiments and validate the scalability and effectiveness of our proposed solutions using multiple real-world datasets.

AB - Many emerging applications such as collaborative editing, multi-player games, or fan-subbing require to form a team of experts to accomplish a task together. Existing research has investigated how to assign workers to such team-based tasks to ensure the best outcome assuming the skills of individual workers to be known. In this work, we investigate how to estimate individual worker's skill based on the outcome of the team-based tasks they have undertaken. We consider two popular skill aggregation functions and estimate the skill of the workers, where skill is either a deterministic value or a probability distribution. We propose effcient solutions for worker skill estimation using continuous and discrete optimization techniques. We present comprehensive experiments and validate the scalability and effectiveness of our proposed solutions using multiple real-world datasets.

UR - http://www.scopus.com/inward/record.url?scp=84953863006&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84953863006&partnerID=8YFLogxK

U2 - 10.14778/2809974.2809977

DO - 10.14778/2809974.2809977

M3 - Chapter

AN - SCOPUS:84953863006

VL - 8

SP - 1142

EP - 1153

BT - Proceedings of the VLDB Endowment

A2 - Claramunt, Christophe

A2 - Saltenis, Simonas

A2 - Li, Ki-Joune

PB - Association for Computing Machinery

T2 - 3rd Workshop on Spatio-Temporal Database Management, STDBM 2006, Co-located with the 32nd International Conference on Very Large Data Bases, VLDB 2006

Y2 - 11 September 2006 through 11 September 2006

ER -

Worker skill estimation in team-based tasks

Abstract

Other

All Science Journal Classification (ASJC) codes

Access to Document

Other files and links

Fingerprint

Cite this