Towards a unified spatial crowdsourcing platform

Christopher Jonathan, Mohamed Mokbel

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper provides the vision of a unified spatial crowdsourcing platform that is designed to efficiently tackle different types of spatial tasks which have been gaining a lot of popularity in recent years. Several examples of spatial tasks are ride-sharing services, delivery services, translation tasks, and crowd-sensing tasks. While existing crowdsourcing platforms, such as Amazon Mechanical Turk and Upwork, are widely used to solve lots of general tasks, e.g., image labeling; using these marketplaces to solve spatial tasks results in low quality results. This paper identifies a set of characteristics for a unified spatial crowdsourcing environment and provides the core components of the platform that are required to empower the capability in solving different types of spatial tasks.

Original languageEnglish
Title of host publicationAdvances in Spatial and Temporal Databases - 15th International Symposium, SSTD 2017, Proceedings
PublisherSpringer Verlag
Pages379-383
Number of pages5
ISBN (Print)9783319643663
DOIs
Publication statusPublished - 1 Jan 2017
Externally publishedYes
Event15th International Symposium on Spatial and Temporal Databases, SSTD 2017 - Arlington, United States
Duration: 21 Aug 201723 Aug 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10411 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other15th International Symposium on Spatial and Temporal Databases, SSTD 2017
CountryUnited States
CityArlington
Period21/8/1723/8/17

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Jonathan, C., & Mokbel, M. (2017). Towards a unified spatial crowdsourcing platform. In Advances in Spatial and Temporal Databases - 15th International Symposium, SSTD 2017, Proceedings (pp. 379-383). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10411 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-64367-0_20