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A Semi-automatic Approach for Tech Mining and Interactive Taxonomy Visualization

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Data Analytics for Renewable Energy Integration (DARE 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10097))

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Abstract

For research directors and other stakeholders, being able to identify emerging technologies and evaluate the comparative advantages and future potentials of these technologies is the highest importance. In previous work, we had proposed a fully automatic, taxonomy-based framework for identifying technologies that are in the early stages of growth and for visualizing their interrelationships. Although this method was very promising, it was apparent that when using a fully automatic process that some level of subjectivity and inconsistency was difficult to avoid. The current work addresses these shortcomings by developing a semi-automatic platform that would allow the incorporation of expert feedback into the tech mining process. To achieve this, a unified web-based application was implemented which combines the analytical techniques proposed in the previous studies with an interactive visualization experience. The proposed approach is evaluated by domain experts and appears to be capable of generating informative and accurate visualizations of early growth technologies.

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Authors and Affiliations

  1. Electrical Engineering and Computer Science, Masdar Institute of Science and Technology, Abu Dhabi, United Arab Emirates

    Ioannis Karakatsanis, Alexandros Tsoupos & Wei Lee Woon

Authors
  1. Ioannis Karakatsanis
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  2. Alexandros Tsoupos
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  3. Wei Lee Woon
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Corresponding author

Correspondence to Ioannis Karakatsanis .

Editor information

Editors and Affiliations

  1. Masdar Institute of Science and Technology , Abu Dhabi, United Arab Emirates

    Wei Lee Woon

  2. Electrical Eng. & Computer Science, Masdar Inst. of Science &Technology Electrical Eng. & Computer Science, Abu Dhabi, United Arab Emirates

    Zeyar Aung

  3. Informatik, Universität Oldenburg Informatik, Oldenburg, Germany

    Oliver Kramer

  4. Massachusetts Institute of Technology , Cambridge, Massachusetts, USA

    Stuart Madnick

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Karakatsanis, I., Tsoupos, A., Woon, W.L. (2017). A Semi-automatic Approach for Tech Mining and Interactive Taxonomy Visualization. In: Woon, W., Aung, Z., Kramer, O., Madnick, S. (eds) Data Analytics for Renewable Energy Integration. DARE 2016. Lecture Notes in Computer Science(), vol 10097. Springer, Cham. https://doi.org/10.1007/978-3-319-50947-1_10

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  • DOI: https://doi.org/10.1007/978-3-319-50947-1_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-50946-4

  • Online ISBN: 978-3-319-50947-1

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