A Framework for Scalable Correlation of Spatio-temporal Event Data

Conference paper

DOI: 10.1007/978-3-319-20424-6_2

Part of the Lecture Notes in Computer Science book series (LNCS, volume 9147)
Cite this paper as:
Hagedorn S., Sattler KU., Gertz M. (2015) A Framework for Scalable Correlation of Spatio-temporal Event Data. In: Maneth S. (eds) Data Science. BICOD 2015. Lecture Notes in Computer Science, vol 9147. Springer, Cham

Abstract

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.

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

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

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