e-Ciencias de la Información ISSN electrónico: 1659-4142

OAI: https://revistas.ucr.ac.cr/index.php/eciencias/oai
Normalization by second order graphs: A visual alternative to simplify systems
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Keywords

Armstrong's Axioms
normalization of relational databases
complexity reduction
break cycles and detect patterns
axiomas de Armstrong
normalización de base de datos relacionales
reducción de la complejidad
romper ciclos y romper patrones

How to Cite

Muñoz Garro, E. (2021). Normalization by second order graphs: A visual alternative to simplify systems: A visual alternative to simplify systems. E-Ciencias De La Información, 11(1). https://doi.org/10.15517/eci.v11i1.38790

Abstract

This issue stems from the need for tools to analyze and make decisions around complex systems, where they apply the rules for linearly dependent sets, with the purpose of providing a visual tool, which serves to support complexity reduction processes. Two great precedents are Armstrong's Axioms, which has been applied from its publication to the present for database normalization, the other is set theory, a fundamental pillar of the Structured Query Language; based on them, together with the second-order logic, which adds qualifiers for subsets or properties, this work has been prepared, with an explanatory metrology with a qualitative approach, in an axiomatic system. As a result, a support tool has been provided to analyze complex systems naturally, by breaking cycles and detecting patterns, without interfering with existing models; however, for large systems it can be difficult to address it in its entirety, so it is recommended to divide by subsystems. With this work a technique has been accomplished, repeatable by anyone, but with a strong theoretical foundation. This work has great utility for the normalization of relational databases and an enormous potential for application in the design of systems beyond computational systems, it is also useful for understanding dependencies by their axiomatic nature.

https://doi.org/10.15517/eci.v11i1.38790
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References

Armstrong, W., Nakamura, Y., & Rudnicki, P. (2002). Armstrong’s axioms. Journal of formalized Mathematics, 14(1). DOI:10.1.1.77.8348

Attallah, B. (2017). Simplifying Database Normalization within a Visual Interactive Simulation Model. International Journal Of Database Management Systems, 9(3), 57-69. doi: 10.5121/ijdms.2017.9304

Bondy, J. A., & Murty (1976) Graph theory with applications. Retrieved from https://pdfs.semanticscholar.org/56a1/3467a3cfb7a9ec00b7f3ed5e953324225233.pdf

Frisendal, T. (2020). Visual Normalization – Use the Power of the Dependencies. Retrieved from https://www.dataversity.net/visual-normalization-use-power-dependencies/#

Kumar, D., Raj, A., & Dharanipragada, J. (2017). GraphSteal: Dynamic Re-Partitioning for Efficient Graph Processing in Heterogeneous Clusters. In 2017 IEEE 10Th International Conference On Cloud Computing (CLOUD). doi: 10.1109/cloud.2017.63

Ren, J., Schneider, J., Ovsjanikov, M., & Wonka, P. (2017). Joint Graph Layouts for Visualizing Collections of Segmented Meshes. In IEEE transactions on visualization and computer graphics, 24(9), 2546-2558. DOI: 10.1109/TVCG.2017.2751473.

Shi, Q., Liu, G., Zheng, K., Liu, A., Li, Z., Zhao, L., & Zhou, X. (2017). Multi-Constrained Top-K Graph Pattern Matching in Contextual Social Graphs. In 2017 IEEE International Conference On Web Services (ICWS). DOI: 10.1109/icws.2017.69

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Copyright (c) 2020 Edward Muñoz Garro

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