Abstract—In modern data-driven analysis it becomes quite typical to process not only the datasets you own, but to collaborate with other organizations to receive data and analysis results from them as well. It is performed to achieve much more accurate analysis results, make better predictions, and be able to provide better decision-support mechanisms. However, to analyze data in a cross-organizational environment is not the same as to analyze your own data: there are many limitations and conditions from the collaborators to allow access to their data and/or analysis models. This paper presents a methodology called CO-ARCH dealing with the process of choosing the suitable data-driven architectures for collaboration on data analysis between different organizations having their own conditions and limitations.
Index Terms—Big data, collaborative AI, data science, multi-organizational data analysis, methodology.
B. D. Van Der Waaij, E. Lazovik, T. Albers, M. R. Vonder are with TNO (the Netherlands Organization for Applied Scientific Research), Groningen, 9727DW, Netherlands (e-mail: bram.vanderwaaij@tno.nl, elena.lazovik@tno.nl, toon.albers@tno.nl, matthijs.vonder@tno.nl).
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Cite: B. D. Van Der Waaij, E. Lazovik, T. Albers, and M. R. Vonder, "CO-ARCH: Methodology for COllaborative ARCHitectures for Cross-organizational Data Analysis," International Journal of Modeling and Optimization vol. 10, no. 1, pp. 13-19, 2020.
Copyright © 2020 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
(CC BY 4.0).
