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Taming Uncertainty in Big Data


  • Johannes Bendler


  • Sebastian Wagner


  • Tobias Brandt


  • Dirk Neumann



While the classic definition of Big Data included the dimensions volume, velocity, and variety, a fourth dimension, veracity, has recently come to the attention of researchers and practitioners. The increasing amount of user-generated data associated with the rise of social media emphasizes the need for methods to deal with the uncertainty inherent to these data sources. In this paper we address one aspect of uncertainty by developing a new methodology to establish the reliability of user-generated data based upon causal links with recurring patterns. We associate a large data set of geo-tagged Twitter messages in San Francisco with points of interest, such as bars, restaurants, or museums, within the city. This model is validated by causal relationships between a point of interest and the amount of messages in its vicinity. We subsequently analyze the behavior of these messages over time using a jackknifing procedure to identify categories of points of interest that exhibit consistent patterns over time. Ultimately, we condense this analysis into an indicator that gives evidence on the certainty of a data set based on these causal relationships and recurring patterns in temporal and spatial dimensions. Copyright Springer Fachmedien Wiesbaden 2014

Suggested Citation

  • Johannes Bendler & Sebastian Wagner & Tobias Brandt & Dirk Neumann, 2014. "Taming Uncertainty in Big Data," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 6(5), pages 279-288, October.
  • Handle: RePEc:spr:binfse:v:6:y:2014:i:5:p:279-288
    DOI: 10.1007/s12599-014-0342-4

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    References listed on IDEAS

    1. Robert E. Kraut & Ronald E. Rice & Colleen Cool & Robert S. Fish, 1998. "Varieties of Social Influence: The Role of Utility and Norms in the Success of a New Communication Medium," Organization Science, INFORMS, vol. 9(4), pages 437-453, August.
    2. Sargent, R P & Shepard, R M & Glantz, Stanton A. Ph.D., 2004. "Reduced incidence of admissions for myocardial infarction associated with public smoking ban: before and after study," University of California at San Francisco, Center for Tobacco Control Research and Education qt3276d6r6, Center for Tobacco Control Research and Education, UC San Francisco.
    3. Xiaoquan (Michael) Zhang & Feng Zhu, 2011. "Group Size and Incentives to Contribute: A Natural Experiment at Chinese Wikipedia," American Economic Review, American Economic Association, vol. 101(4), pages 1601-1615, June.
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