by Taques, Fernando Henrique; Chasco, Coro; Taques, Flavio;

Abstract: Dataset accompanying the publication "Integrating big data with KNIME as an alternative without programming code: an application to the PATSTAT patent database ". 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.

Description: How to cite the database (APA style):
Taques, F.H.; Chasco, C. & Taques, F. (2024) Integrating big data with KNIME as an alternative without programming code: an application to the PATSTAT patent database [Data set] (doi: 10.23728/b2share.645943e855924aa299a2e2dc873ce530)
Source:
Taques, F.H.; Chasco, C. & Taques, F. (2024) Integrating big data with KNIME as an alternative without programming code: an application to the PATSTAT patent database. Journal of Geographical Systems (doi: 10.1007/s10109-024-00445-0).

Disciplines: 2.5.6 → Economics → Computational economics; 4.1.16.1 → Information science → Data management; 4.1.16.3.1 → Database → Relational database; 5.3.3 → Business → Business analysis

Keywords: Big data; EPO; KNIME; MySQL; PATSTAT;

DOI: 10.23728/b2share.645943e855924aa299a2e2dc873ce530

PID: 11304/583ee0e2-c982-4fd1-9269-e9a430f4a98e