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Pattern Based Decision Tree Analysis for Risk Detection in Smart Cities

Eibl, Maximilian ; Gaedke, Martin (Hrsg). Informatik 2017 : 25.- 29. September 2017 Chemnitz, Deutschland. Bonn: Gesellschaft für Informatik e.V. (GI) 2017 S. 931 - 938 (Gesellschaft für Informatik: GI-Edition / Proceedings ; 275)

Erscheinungsjahr: 2017

ISBN/ISSN: 978-3-88579-669-5

Publikationstyp: Buchbeitrag

Sprache: Deutsch

Doi/URN: https://doi.org/10.18420/in2017_95

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Inhaltszusammenfassung


Increasing amounts of data on living environments and human interactions are becoming available. Their potential for valuable services improving the wellbeing of individuals is large and growing. This calls for an investigation of algorithms and system architectures that support possible use cases. In this paper we outline how pattern based decision tree analyses can be applied to the identification of risks caused by time-dependent effects from multiple influencing factors. For this purpose ...Increasing amounts of data on living environments and human interactions are becoming available. Their potential for valuable services improving the wellbeing of individuals is large and growing. This calls for an investigation of algorithms and system architectures that support possible use cases. In this paper we outline how pattern based decision tree analyses can be applied to the identification of risks caused by time-dependent effects from multiple influencing factors. For this purpose we apply the method to open data on car accidents and weather conditions. We also show how such systems can take advantage from up-to-date in-memory technology. » weiterlesen» einklappen

  • Data Mining, In-Memory Computing, Smart City Services

Autoren


Scholz, Matthias (Autor)

Klassifikation


DFG Fachgebiet:
Informatik

DDC Sachgruppe:
Informatik

Verknüpfte Personen


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