Manuscript received February 15, 2024; revised April 7, 2024; accepted April 23, 2024; published August 20, 2024

Abstract—To efficiently store data where the relationships between individual objects are essential, the use of a graph database model is recommended. After storing the data, it is necessary to further analyze it using statistical methods or visualize it within the context of exploratory data analysis. Such visualization is crucial for understanding the structure and content of the database. However, commonly used visualization tools often fall short in terms of interactivity and effectiveness. The main objective of the presented work is the design and implementation of a novel model for the visualization of data structures stored in graph databases with the use of two natural graphical models—the standard topological layout of the database and the so-called clustered layout of a graph database. The presented graphical models are focused on interactive visualization, mainly scaling of visualization and development of database objects, and principles of effective visualization. Implementation of the proposed approach was evaluated via case studies on three model graph databases of various sizes—Messaging database (16 objects, 16 relationships), Library database (16 objects, 32 relationships) and Movie database (171 objects, 253 relationships). Compared to the standard Neo4j tool for the visual representation of property graphs in graph databases, the proposed model presents improvement in terms of the number of visualization models, effectivity of the visualization, and development of objects in the visualized database.

Keywords—visualization, graph databases, clustered visualization layout, data representation

Cite: Adam Dudáš and Adam Kleinedler, "Effective Visualization of Data Structures in Graph Databases," Journal of Image and Graphics, Vol. 12, No. 3, pp. 283-291, 2024.

Copyright © 2024 by the authors. This is an open access article distributed under the Creative Commons Attribution License (CC BY-NC-ND 4.0), which permits use, distribution and reproduction in any medium, provided that the article is properly cited, the use is non-commercial and no modifications or adaptations are made.