New data paradigms: From the crowd and back

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

Abstract

Knowledge generation from citizens is becoming both more feasible as well as important. Data directly from individuals can be critical as it can add information beyond what is available otherwise. Crowdsourced data also is very amenable in open data efforts given the nature of its generation. In this talk I will describe several efforts in which we are generating crowdsourced knowledge from open data and using it to more readily improve knowledge in public health.

Original languageEnglish (US)
Title of host publicationProceedings - 2017 IEEE International Conference on Big Data, Big Data 2017
EditorsJian-Yun Nie, Zoran Obradovic, Toyotaro Suzumura, Rumi Ghosh, Raghunath Nambiar, Chonggang Wang, Hui Zang, Ricardo Baeza-Yates, Ricardo Baeza-Yates, Xiaohua Hu, Jeremy Kepner, Alfredo Cuzzocrea, Jian Tang, Masashi Toyoda
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3979-3980
Number of pages2
ISBN (Electronic)9781538627143
DOIs
StatePublished - Jul 1 2017
Event5th IEEE International Conference on Big Data, Big Data 2017 - Boston, United States
Duration: Dec 11 2017Dec 14 2017

Publication series

NameProceedings - 2017 IEEE International Conference on Big Data, Big Data 2017
Volume2018-January

Other

Other5th IEEE International Conference on Big Data, Big Data 2017
CountryUnited States
CityBoston
Period12/11/1712/14/17

Keywords

  • Crowdsourcing
  • inference algorithms
  • public healthcare

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Hardware and Architecture
  • Information Systems
  • Information Systems and Management
  • Control and Optimization

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