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Integrating big data with KNIME as an alternative without programming code: an application to the PATSTAT patent database

Author

Listed:
  • Fernando H. Taques

    (Universidad Autónoma de Madrid
    Federal University of Pernambuco)

  • Coro Chasco

    (Universidad Autónoma de Madrid)

  • Flávio H. Taques

    (Faculdades Metropolitanas Unidas)

Abstract

Accessing massive datasets can be challenging for users unfamiliar with programming codes. Combining Konstanz Information Miner (KNIME) and MySQL tools on standard configuration equipment allows for addressing this issue. This research proposal aims to present a methodology that describes the necessary configuration steps in both tools and the required manipulation in KNIME to transmit the information to the MySQL environment for further processing in a database management system (DBMS). In addition, we propose a procedure so that the use of this point-and-click software in research work can gain in reproducibility and, therefore, in credibility in the scientific community. To achieve this, we will use a big database regarding patent applications as a reference, the PATSTAT Global 2023, provided by the European Patent Office (EPO). As well known, patent data can be a valuable source for understanding innovation dynamics and technological trends, whether for studies on companies, sectors, nations or even regions, at aggregated and disaggregated levels.

Suggested Citation

  • Fernando H. Taques & Coro Chasco & Flávio H. Taques, 2025. "Integrating big data with KNIME as an alternative without programming code: an application to the PATSTAT patent database," Journal of Geographical Systems, Springer, vol. 27(1), pages 31-61, January.
    • Handle: RePEc:kap:jgeosy:v:27:y:2025:i:1:d:10.1007_s10109-024-00445-0
    DOI: 10.1007/s10109-024-00445-0
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    References listed on IDEAS

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    1. Rafael H. M. Pereira & Pedro R. Andrade & João Pedro Bazzo Vieira, 2023. "Exploring the time geography of public transport networks with the gtfs2gps package," Journal of Geographical Systems, Springer, vol. 25(3), pages 453-466, July.
    2. Jonathan Reades & Sergio J. Rey, 2021. "Geographical Python Teaching Resources: geopyter," Journal of Geographical Systems, Springer, vol. 23(4), pages 579-597, October.
    3. Jojo Jacob, 2013. "PATSTAT database for patent-based research," Innovation and Development, Taylor & Francis Journals, vol. 3(2), pages 313-315, October.
    4. Andrés Vallone & Coro Chasco & Beatriz Sánchez, 2020. "Strategies to access web-enabled urban spatial data for socioeconomic research using R functions," Journal of Geographical Systems, Springer, vol. 22(2), pages 217-239, April.
    5. Gaétan de Rassenfosse & Hélène Dernis & Geert Boedt, 2014. "An Introduction to the Patstat Database with Example Queries," Australian Economic Review, The University of Melbourne, Melbourne Institute of Applied Economic and Social Research, vol. 47(3), pages 395-408, September.
    6. Kathryn Rudie Harrigan & Maria Chiara DiGuardo, 2017. "Sustainability of patent-based competitive advantage in the U.S. communications services industry," The Journal of Technology Transfer, Springer, vol. 42(6), pages 1334-1361, December.
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    More about this item

    Keywords

    Big data; EPO; KNIME; MySQL; Patent; PATSTAT;
    All these keywords.

    JEL classification:

    • C80 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - General
    • C88 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Other Computer Software
    • O30 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - General

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