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HypGraphs: An Approach for Analysis and Assessment of Graph-Based and Sequential Hypotheses

  • Martin Atzmueller
  • Andreas Schmidt
  • Benjamin Kloepper
  • David Arnu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10312)

Abstract

The analysis of sequential patterns is a prominent research topic. In this paper, we provide a formalization of a graph-based approach, such that a directed weighted graph/network can be extended using a sequential state transformation function, that “interprets” the network in order to model state transition matrices. We exemplify the approach for deriving such interpretations, in order to assess these and according hypotheses in an industrial application context. Specifically, we present and discuss results of applying the proposed approach for topology and anomaly analytics in a large-scale real-world sensor-network.

Notes

Acknowledgements

This work was funded by the BMBF project FEE under grant number 01IS14006. We wish to thank Leon Urbas (TU Dresden) and Florian Lemmerich (GESIS, Cologne) for helpful discussions, also concerning Florian’s implementation of HypTrails (https://bitbucket.org/florian_lemmerich/hyptrails4j) [20].

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Martin Atzmueller
    • 1
  • Andreas Schmidt
    • 1
  • Benjamin Kloepper
    • 2
  • David Arnu
    • 3
  1. 1.Research Center for Information System DesignUniversity of KasselKasselGermany
  2. 2.ABB Corporate Research Center GermanyLadenburgGermany
  3. 3.RapidMiner GmbHDortmundGermany

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