Earthquake Investigation and Visual Cognizance of Multivariate Temporal Tabular Data Using Machine Learning

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2019
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CHANG, Remco, ed., Daniel A. KEIM, ed., Ross MACIEJEWSKI, ed.. 2019 IEEE Conference on Visual Analytics Science and Technology (VAST) : Proceedings. Piscataway, NJ: IEEE, 2019, pp. 136-137. ISBN 978-1-72812-284-7
Zusammenfassung

This paper presents our tool for the Vast Challenge 2019 Mini Challenge 1 (MC1). It will give an overview of the approach of data preprocessing techniques used for the given dataset and it will introduce our application which is built considering the requirements and questions to be answered for the MC1. This application consists of Machine Learning techniques and Information Visualization techniques such as Integrated Spatial Uncertainty Visualization as shown in this paper [1] to convey the needed information to the end users. To show the usefulness of this application we give examples of analysis.

Zusammenfassung in einer weiteren Sprache
Fachgebiet (DDC)
004 Informatik
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Machine Learning, Visual Analytics
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2019 IEEE Conference on Visual Analytics Science and Technology (VAST), 20. Okt. 2019 - 25. Okt. 2019, Vancouver, BC, Canada
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Zitieren
ISO 690MAJUMDAR, Arjun, Gent YMERI, Sebastian STRUMBELJ, Juri F. BUCHMÜLLER, Udo SCHLEGEL, Daniel A. KEIM, 2019. Earthquake Investigation and Visual Cognizance of Multivariate Temporal Tabular Data Using Machine Learning. 2019 IEEE Conference on Visual Analytics Science and Technology (VAST). Vancouver, BC, Canada, 20. Okt. 2019 - 25. Okt. 2019. In: CHANG, Remco, ed., Daniel A. KEIM, ed., Ross MACIEJEWSKI, ed.. 2019 IEEE Conference on Visual Analytics Science and Technology (VAST) : Proceedings. Piscataway, NJ: IEEE, 2019, pp. 136-137. ISBN 978-1-72812-284-7
BibTex
@inproceedings{Majumdar2019Earth-50591,
  year={2019},
  title={Earthquake Investigation and Visual Cognizance of Multivariate Temporal Tabular Data Using Machine Learning},
  isbn={978-1-72812-284-7},
  publisher={IEEE},
  address={Piscataway, NJ},
  booktitle={2019 IEEE Conference on Visual Analytics Science and Technology (VAST) : Proceedings},
  pages={136--137},
  editor={Chang, Remco and Keim, Daniel A. and Maciejewski, Ross},
  author={Majumdar, Arjun and Ymeri, Gent and Strumbelj, Sebastian and Buchmüller, Juri F. and Schlegel, Udo and Keim, Daniel A.}
}
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