Crowdsourcing high-quality structured data

Harry Halpin; Ioanna Lykourentzou

doi:10.1007/978-3-030-11680-4_29

Crowdsourcing high-quality structured data

Harry Halpin^*, Ioanna Lykourentzou

^*Corresponding author for this work

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

Abstract

One of the most difficult problems faced by consumers of semi-structured and structured data on the Web is how to discover or create the data they need. On the other hand, the producers of Web data do not have any (semi)automated way to align their data production with consumer needs. In this paper we formalize the problem of a data marketplace, hypothesize that one can quantify the value of semi-structured and structured data given a set of consumers, and that this quantification can be applied on both existing data-sets and data-sets that need to be created. Furthermore, we provide an algorithm for showing how the production of this data can be crowd-sourced while assuring the consumer a certain level of quality. Using real-world empirical data collected via data producers and consumers, we simulate a crowd-sourced data marketplace with quality guarantees.

Original language	English
Title of host publication	Information Management and Big Data - 5th International Conference, SIMBig 2018, Proceedings
Editors	Denisse Muñante, Hugo Alatrista-Salas, Juan Antonio Lossio-Ventura
Publisher	Springer
Pages	304-319
Number of pages	16
ISBN (Print)	9783030116798
DOIs	https://doi.org/10.1007/978-3-030-11680-4_29
Publication status	Published - 2019
Event	5th International Conference on Information Management and Big Data, SIMBig 2018 - Lima, Peru Duration: 3 Sept 2018 → 5 Sept 2018

Publication series

Name	Communications in Computer and Information Science
Volume	898
ISSN (Print)	1865-0929

Conference

Conference	5th International Conference on Information Management and Big Data, SIMBig 2018
Country/Territory	Peru
City	Lima
Period	3/09/18 → 5/09/18

Keywords

Crowdsourcing
Human computation
Resource allocation
Structured data

Access to Document

10.1007/978-3-030-11680-4_29

978-3-030-11680-4_29Final published version, 1.42 MBLicence: Taverne

Cite this

Halpin, H., & Lykourentzou, I. (2019). Crowdsourcing high-quality structured data. In D. Muñante, H. Alatrista-Salas, & J. A. Lossio-Ventura (Eds.), Information Management and Big Data - 5th International Conference, SIMBig 2018, Proceedings (pp. 304-319). (Communications in Computer and Information Science; Vol. 898). Springer. https://doi.org/10.1007/978-3-030-11680-4_29

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title = "Crowdsourcing high-quality structured data",

abstract = "One of the most difficult problems faced by consumers of semi-structured and structured data on the Web is how to discover or create the data they need. On the other hand, the producers of Web data do not have any (semi)automated way to align their data production with consumer needs. In this paper we formalize the problem of a data marketplace, hypothesize that one can quantify the value of semi-structured and structured data given a set of consumers, and that this quantification can be applied on both existing data-sets and data-sets that need to be created. Furthermore, we provide an algorithm for showing how the production of this data can be crowd-sourced while assuring the consumer a certain level of quality. Using real-world empirical data collected via data producers and consumers, we simulate a crowd-sourced data marketplace with quality guarantees.",

keywords = "Crowdsourcing, Human computation, Resource allocation, Structured data",

author = "Harry Halpin and Ioanna Lykourentzou",

note = "Publisher Copyright: {\textcopyright} 2019, Springer Nature Switzerland AG.; 5th International Conference on Information Management and Big Data, SIMBig 2018 ; Conference date: 03-09-2018 Through 05-09-2018",

year = "2019",

doi = "10.1007/978-3-030-11680-4_29",

language = "English",

isbn = "9783030116798",

series = "Communications in Computer and Information Science",

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pages = "304--319",

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Halpin, H & Lykourentzou, I 2019, Crowdsourcing high-quality structured data. in D Muñante, H Alatrista-Salas & JA Lossio-Ventura (eds), Information Management and Big Data - 5th International Conference, SIMBig 2018, Proceedings. Communications in Computer and Information Science, vol. 898, Springer, pp. 304-319, 5th International Conference on Information Management and Big Data, SIMBig 2018, Lima, Peru, 3/09/18. https://doi.org/10.1007/978-3-030-11680-4_29

Crowdsourcing high-quality structured data. / Halpin, Harry; Lykourentzou, Ioanna.
Information Management and Big Data - 5th International Conference, SIMBig 2018, Proceedings. ed. / Denisse Muñante; Hugo Alatrista-Salas; Juan Antonio Lossio-Ventura. Springer, 2019. p. 304-319 (Communications in Computer and Information Science; Vol. 898).

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

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N2 - One of the most difficult problems faced by consumers of semi-structured and structured data on the Web is how to discover or create the data they need. On the other hand, the producers of Web data do not have any (semi)automated way to align their data production with consumer needs. In this paper we formalize the problem of a data marketplace, hypothesize that one can quantify the value of semi-structured and structured data given a set of consumers, and that this quantification can be applied on both existing data-sets and data-sets that need to be created. Furthermore, we provide an algorithm for showing how the production of this data can be crowd-sourced while assuring the consumer a certain level of quality. Using real-world empirical data collected via data producers and consumers, we simulate a crowd-sourced data marketplace with quality guarantees.

AB - One of the most difficult problems faced by consumers of semi-structured and structured data on the Web is how to discover or create the data they need. On the other hand, the producers of Web data do not have any (semi)automated way to align their data production with consumer needs. In this paper we formalize the problem of a data marketplace, hypothesize that one can quantify the value of semi-structured and structured data given a set of consumers, and that this quantification can be applied on both existing data-sets and data-sets that need to be created. Furthermore, we provide an algorithm for showing how the production of this data can be crowd-sourced while assuring the consumer a certain level of quality. Using real-world empirical data collected via data producers and consumers, we simulate a crowd-sourced data marketplace with quality guarantees.

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ER -

Crowdsourcing high-quality structured data

Abstract

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Keywords

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