Maximizing benefits from crowdsourced data

被引:0
作者
Geoffrey Barbier
Reza Zafarani
Huiji Gao
Gabriel Fung
Huan Liu
机构
[1] Air Force Research Laboratory,
[2] Arizona State University,undefined
[3] IGNGAB Lab,undefined
来源
Computational and Mathematical Organization Theory | 2012年 / 18卷
关键词
Crowdsourcing; Event maps; Community maps; Crisis maps; Social media; Data mining; Machine learning; Humanitarian Aid and Disaster Relief (HADR);
D O I
暂无
中图分类号
学科分类号
摘要
Crowds of people can solve some problems faster than individuals or small groups. A crowd can also rapidly generate data about circumstances affecting the crowd itself. This crowdsourced data can be leveraged to benefit the crowd by providing information or solutions faster than traditional means. However, the crowdsourced data can hardly be used directly to yield usable information. Intelligently analyzing and processing crowdsourced information can help prepare data to maximize the usable information, thus returning the benefit to the crowd. This article highlights challenges and investigates opportunities associated with mining crowdsourced data to yield useful information, as well as details how crowdsource information and technologies can be used for response-coordination when needed, and finally suggests related areas for future research.
引用
收藏
页码:257 / 279
页数:22
相关论文
共 50 条
  • [21] A Trustworthiness Model for Crowdsourced and Crowdsensed Data
    Prandi, Catia
    Ferretti, Stefano
    Mirri, Silvia
    Salomoni, Paola
    2015 IEEE TRUSTCOM/BIGDATASE/ISPA, VOL 1, 2015, : 1261 - 1266
  • [22] Responsible processing of crowdsourced tourism data
    Leal, Fatima
    Malheiro, Benedita
    Veloso, Bruno
    Carlos Burguillo, Juan
    JOURNAL OF SUSTAINABLE TOURISM, 2021, 29 (05) : 774 - 794
  • [23] Active Learning Based on Crowdsourced Data
    Boinski, Tomasz Maria
    Szymanski, Julian
    Krauzewicz, Agata
    APPLIED SCIENCES-BASEL, 2022, 12 (01):
  • [24] Investigating and Mitigating Biases in Crowdsourced Data
    Hettiachchi, Danula
    Sanderson, Mark
    Goncalves, Jorge
    Hosio, Simo
    Kazai, Gabriella
    Lease, Matthew
    Schaekermann, Mike
    Yilmaz, Emine
    CONFERENCE COMPANION PUBLICATION OF THE 2021 COMPUTER SUPPORTED COOPERATIVE WORK AND SOCIAL COMPUTING, CSCW 2021 COMPANION, 2021, : 331 - 334
  • [25] Millimeter-Level Plant Disease Detection From Aerial Photographs via Deep Learning and Crowdsourced Data
    Wiesner-Hanks, Tyr
    Wu, Harvey
    Stewart, Ethan L.
    DeChant, Chad
    Kaczmar, Nicholas
    Lipson, Hod
    Gore, Michael A.
    Nelson, Rebecca J.
    FRONTIERS IN PLANT SCIENCE, 2019, 10
  • [26] Evaluating the services and facilities of European cities using crowdsourced place data
    Spyratos, Spyridon
    Stathakis, Demetris
    ENVIRONMENT AND PLANNING B-URBAN ANALYTICS AND CITY SCIENCE, 2018, 45 (04) : 733 - 750
  • [27] A System Design Perspective for Business Growth in a Crowdsourced Data Labeling Practice
    Hajipour, Vahid
    Jalali, Sajjad
    Santos-Arteaga, Francisco Javier
    Vazifeh Noshafagh, Samira
    Di Caprio, Debora
    ALGORITHMS, 2024, 17 (08)
  • [28] Urbangene Project Experience from a Crowdsourced Mapping Campaign
    Ingensand, Jens
    Nappez, Marion
    Joost, Stephane
    Widmer, Ivo
    Ertz, Olivier
    Rappo, Daniel
    2015 1ST INTERNATIONAL CONFERENCE ON GEOGRAPHICAL INFORMATION SYSTEMS THEORY, APPLICATIONS AND MANAGEMENT (GISTAM), 2015, : 178 - 184
  • [29] spark-crowd: A Spark Package for Learning from Crowdsourced Big Data
    Rodrigo, Enrique G.
    Aledo, Juan A.
    Gamez, Jose A.
    JOURNAL OF MACHINE LEARNING RESEARCH, 2019, 20
  • [30] Quality assessment of crowdsourced social media data for urban flood management
    Songchon, Chanin
    Wright, Grant
    Beevers, Lindsay
    COMPUTERS ENVIRONMENT AND URBAN SYSTEMS, 2021, 90