A Framework for Scalable Correlation of Spatio-temporal Event Data

  • Stefan HagedornEmail author
  • Kai-Uwe Sattler
  • Michael Gertz
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9147)


Spatio-temporal event data do not only arise from sensor readings, but also in information retrieval and text analysis. However, such events extracted from a text corpus may be imprecise in both dimensions. In this paper we focus on the task of event correlation, i.e., finding events that are similar in terms of space and time. We present a framework for Apache Spark that provides correlation operators that can be configured to deal with such imprecise event data.


Apache Spark Event Data Model Skyline Processing Resilient Distributed Datasets (RDD) Geographic Component 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This work was funded by the DFG under grant no. SA782/22.


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Stefan Hagedorn
    • 1
    Email author
  • Kai-Uwe Sattler
    • 1
  • Michael Gertz
    • 2
  1. 1.Technische Universität IlmenauIlmenauGermany
  2. 2.Heidelberg UniversityHeidelbergGermany

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