Abstract
Crowdsourcing allows us to employ collective human intelligence and resources in completing tasks in a wide variety of domains, such as mapping, translation, emergency response, and even fund raising. It first involves identification of a problem that can be solved using crowdsourcing and then its decomposition into tasks that workers can finish in a timely manner. Worker engagement analysis and data quality analysis are done afterwards. Such analysis activities are not supported by current platforms and are done in an ad-hoc fashion leading to duplicate efforts. As a first step towards realizing such analysis mechanisms, we propose a Data mOdel for crOwdsouRcing (DOOR), which is based on a fuzzy Entity-Relationship model in order to capture the uncertainty that is inherent in any crowdsourcing process. To illustrate its application, we have chosen the problem of collection of data about incidents for emergency response.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ahmad, S., Battle, A., Malkani, Z., Kamvar, S.: The jabberwocky programming environment for structured social computing. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 53–64. ACM (2011)
Alonso, O.: Perspectives on infrastructure for crowdsourcing. In: Crowdsourcing for Search and Data Mining (CSDM 2011), p. 7 (2011)
amazon.com: Amazon Mechanical Turk. https://www.mturk.com/. Accessed 24 April 2014
Butler, D.: Crowdsourcing goes mainstream in typhoon response. Nature (2013)
Fuchs-Kittowski, F., Faust, D.: Architecture of mobile crowdsourcing systems. In: Baloian, N., Burstein, F., Ogata, H., Santoro, F., Zurita, G. (eds.) CRIWG 2014. LNCS, vol. 8658, pp. 121–136. Springer, Heidelberg (2014)
Goodchild, M.F.: Citizens as sensors: the world of volunteered geography. GeoJournal 69(4), 211–221 (2007)
Goodchild, M.F., Glennon, J.A.: Crowdsourcing geographic information for disaster response: a research frontier. Int. J. Digit. Earth 3(3), 231–241 (2010)
Heipke, C.: Crowdsourcing geospatial data. ISPRS J. Photogrammetry Remote Sens. 65(6), 550–557 (2010)
Kulkarni, A.P., Can, M., Hartmann, B.: Turkomatic: automatic recursive task and workflow design for mechanical turk. In: CHI 2011 Extended Abstracts on Human Factors in Computing Systems, pp. 2053–2058. ACM (2011)
Little, G., Chilton, L.B., Goldman, M., Miller, R.C.: Turkit: human computation algorithms on mechanical turk. In: Proceedings of the 23nd Annual ACM Symposium on User Interface Software and Technology, pp. 57–66. ACM (2010)
Mehta, P., Müller, S., Voisard, A.: Movesafe: A framework for transportation mode-based targeted alerting in disaster response. In: Proceedings of the Second ACM SIGSPATIAL International Workshop on Crowdsourced and Volunteered Geographic Information, GEOCROWD 2013, pp. 15–22. ACM, New York (2013). http://doi.acm.org/10.1145/2534732.2534735
Munro, R.: Crowdsourced translation for emergency response in Haiti: the global collaboration of local knowledge. In: AMTA Workshop on Collaborative Crowdsourcing for Translation (2010)
OpenStreetMap Community: OpenStreetMap. http://www.openstreetmap.org/. Accessed 04 April 2014
pybossa.com: Pybossa. https://pybossa.com/. Accessed 01 June 2014
TechCrunch: Help Me Help uses crowdsourcing to make disaster response more efficient. http://techcrunch.com/2013/07/04/. Accessed 24 March 2014
Ushahidi Inc.: Ushahidi. http://ushahidi.com/. Accessed 04 April 2014
Von Ahn, L., Dabbish, L.: Labeling images with a computer game. In: Proceedings of the SIGCHI Conference on Human Factors In Computing Systems, pp. 319–326. ACM (2004)
Von Ahn, L., Maurer, B., McMillen, C., Abraham, D., Blum, M.: recaptcha: Human-based character recognition via web security measures. Science 321(5895), 1465–1468 (2008)
Zheng, L., Shen, C., Tang, L., Li, T., Luis, S., Chen, S.C.: Applying data mining techniques to address disaster information management challenges on mobile devices. In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2011, pp. 283–291. ACM, New York (2011). http://doi.acm.org/10.1145/2020408.2020457
Acknowledgements
This research is carried out in the framework of the GEOCROWD project funded by the European Commission.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Cuong, T.T., Mehta, P., Voisard, A. (2015). DOOR: A Data Model for Crowdsourcing with Application to Emergency Response. In: Giaffreda, R., Cagáňová, D., Li, Y., Riggio, R., Voisard, A. (eds) Internet of Things. IoT Infrastructures. IoT360 2014. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 151. Springer, Cham. https://doi.org/10.1007/978-3-319-19743-2_37
Download citation
DOI: https://doi.org/10.1007/978-3-319-19743-2_37
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-19742-5
Online ISBN: 978-3-319-19743-2
eBook Packages: Computer ScienceComputer Science (R0)