• DocumentCode
    3612859
  • Title

    KNIME an Open Source Solution for Predictive Analytics in the Geosciences [Software and Data Sets]

  • Author

    Feltrin, Leonardo

  • Author_Institution
    Earth Sciences, University of Western Ontario, London, N6A5B7, Canada
  • Volume
    3
  • Issue
    4
  • fYear
    2015
  • Firstpage
    28
  • Lastpage
    38
  • Abstract
    Abstract-KNIME (Konstanz Information Miner) is a modular computational environment, which allowseasy visual assembly, interactive data analysis, and data processing. It is an open source predictive analytics platform (released under the GNU General Public License v3) suited to process a variety of data formats, from basic csv or xlsx files, to more complex data structures such as xml, url and relational databases (e.g., db2, Oracle, MySQL). Surprisingly, it has not seen wide application in the earth sciences. A number of case studies providing examples of geoscience data processing will benefit both the academia and industry, very few geoscience applications are currently reported and these are dominantly in geoinformatics. In particular, the Energy and Mineral Exploration sectors, which make extensive use of Exploratory Data Analysis, Machine Learning (ML) and Data Mining (DM) software for data classification, pattern recognition and predictive modelling, will benefit significantly from KNIME. In contrast to other predictive analytics platforms (e.g., Orange, R, Rapid- Miner, Scikit-learn), what makes KNIME particularly appealing to geoscience applications is its ability to integrate different programming languages in the same workflow environment, some of them like the statistical software R or Matlab are well known in the geoscience community. KNIME is supported by an extensive community of users and developers. Since KNIME is built on top of Eclipse it shares the benefit of a plugin architecture that makes it easily extensible, many custom-built nodes are available and easily accessible through the Community Contributions area.
  • Keywords
    Classification; Data analysis; Data mining; Data visualization; Geoinformatics; Geology; Informatics; Software development;
  • fLanguage
    English
  • Journal_Title
    Geoscience and Remote Sensing Magazine, IEEE
  • Publisher
    ieee
  • ISSN
    2168-6831
  • Type

    jour

  • DOI
    10.1109/MGRS.2015.2496160
  • Filename
    7370021