Abstract

This paper’s main contributions are the comparative analysis of open source business intelli- gence platforms, having as background aim, the selection of the most appropriate one for its integration in an open source low-code platform we are developing. We researched the state-of- the art and selected nine platforms for a first and more general analysis. Out of these nine, four were then selected for a more thorough and detailed analysis and testing. The best two of this set were then selected for the implementation of concrete integration tests with our low-code platform. During the process, one of them revealed to have critical problems and the other was integrated successfully, with functional results achieving the best expectations. Thus, another main contribution of this paper is the identification of the current best candidate open source business intelligence platform for integration with a low-code platform.

Recommended Citation

Aveiro, D., Mendes, J., Pinto, D., & Freitas, V. (2023). A Comparative Analysis of Open-Source Business Intelligence Platforms for Integration with a Low-Code Platform. In A. R. da Silva, M. M. da Silva, J. Estima, C. Barry, M. Lang, H. Linger, & C. Schneider (Eds.), Information Systems Development, Organizational Aspects and Societal Trends (ISD2023 Proceedings). Lisbon, Portugal: Instituto Superior Técnico. ISBN: 978-989-33-5509-1. https://doi.org/10.62036/ISD.2023.2

Paper Type

Short Paper

DOI

10.62036/ISD.2023.2

Additional Files
References_DOI_ISD.2023.2.pdf (67 kB)
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A Comparative Analysis of Open-Source Business Intelligence Platforms for Integration with a Low-Code Platform

This paper’s main contributions are the comparative analysis of open source business intelli- gence platforms, having as background aim, the selection of the most appropriate one for its integration in an open source low-code platform we are developing. We researched the state-of- the art and selected nine platforms for a first and more general analysis. Out of these nine, four were then selected for a more thorough and detailed analysis and testing. The best two of this set were then selected for the implementation of concrete integration tests with our low-code platform. During the process, one of them revealed to have critical problems and the other was integrated successfully, with functional results achieving the best expectations. Thus, another main contribution of this paper is the identification of the current best candidate open source business intelligence platform for integration with a low-code platform.