Visualização 3D para Bancos de Dados Orientados a Grafos: Um Estudo de Caso Utilizando Redes Sociais
Resumo
Um conhecido desafio da comunidade de banco de dados orientado a grafos inclui aspectos de visualização para viabilizar a compreensão quantitativa e qualitativa da informação. Desta forma, o objetivo deste trabalho é aplicar técnicas de visualização 3D para bancos de dados online Neo4J. Técnicas de visualização 3D têm demonstrado utilidade para minimizar o problema da oclusão de dados, além de viabilizar uma maior interatividade do usuário. A ferramenta Graph2Vis com visualização 3D foi implementado com o framework Angular e a biblioteca 3d-force-graph. Um estudo de caso utilizando redes sociais formada por cinco programas de pós-graduação em Computação vinculados a universidades brasileiras ilustra o uso do Graph2Vis.
Palavras-chave:
Grafos, Neo4j, Redes Sociais, Visualização de Grafos
Referências
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Hühne, R., Kessler, V., Fürstberger, A., Kühlwein, S., Platzer, M., Sühnel, J., Lausser, L., and Kestler, H. (2018). 3D Network exploration and visualisation for lifespan data. BMC Bioinformatics, 19(390).
Jo, S., Park, B., Lee, S., and Kim, J. (2021). OLGAVis: On-Line Graph Analysis and Visualization for Bibliographic Information Network. Applied Sciences, 11(9).
Kerzner, E., Lex, A., Sigulinsky, C. L., Urness, T., Jones, B. W., Marc, R. E., and Meyer, M. (2017). Graffinity: Visualizing connectivity in large graphs. Computer Graphics Forum (EuroVis), 36(3):251-260.
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Partl, C., Gratzl, S., Streit, M., Wassermann, A. M., Pfister, H., Schmalstieg, D., and Lex, A. (2016). Pathfinder: Visual analysis of paths in graphs. Computer Graphics Forum (EuroVis ’16), 35(3):71-80.
Rahman, M. and Karim, R. (2016). Comparative study of different methods of social network analysis and visualization. In Int. Conf. Networking Systems & Security.
Rodrigues, J. F., Tong, H., Traina, A. J. M., Faloutsos, C., and Leskovec, J. (2015). Gmine: A system for scalable, interactive graph visualization and mining. In Proc. 32nd Int. Conf. on Very Large Data Bases, page 1195-1198.
Summer, G., Kelder, T., Ono, K., Radonjic, M., Heymans, S., and Demchak, B. (2015). cyNeo4j: connecting Neo4j and Cytoscape. Bioinformatics, 31(23):3868-3869.
Tallat, R., Amir Latif, R. M., Ali, G., Zaheer, A. N., Farhan, M., and Aslam Shah, S. U. (2019). Visualization and analytics of biological data by using different tools and techniques. In IEEE 16th Int. Bhurban Conf. Applied Sci. & Tech., pages 291-303.
Yokoyama, T. T., Okada, M., and Taniguchi, T. (2021). Panacea: Visual exploration system for analyzing trends in annual recruitment using time-varying graphs. PLOS ONE, 16(3):1-22.
Zahoránszky-Kohalmi, G., Sheils, T., and Oprea, T. (2020). SmartGraph: a Network Pharmacology Investigation Platform. Journal of Cheminformatics, 12(5).
Bludau, M.-J., Dörk, M., and Tominski, C. (2021). Unfolding Edges for Exploring Multivariate Edge Attributes in Graphs. In Byska, J., Jänicke, S., and Schmidt, J., editors, EuroVis 2021-Posters. The Eurographics Association.
Bukhari, S. A. C., Pawar, S., Mandell, J., Kleinstein, S. H., and Cheung, K.-H. (2021). LinkedImm: a linked data graph database for integrating immunological data. BMC bioinformatics, 22(S9):105-105.
Carnaz, G., Nogueira, V. B., and Antunes, M. (2021). A graph database representation of portuguese criminal-related documents. Informatics, 8(2).
Chen, Y., Guan, Z., Zhang, R., Du, X., and Wang, Y. (2019). A survey on visualization approaches for exploring association relationships in graph data. J. Visualization, 22.
Farooq, A., Joyia, G. J., Uzair, M., and Akram, U. (2018). Detection of influential nodes using social networks analysis based on network metrics. In 2018 International Conference on Computing, Mathematics and Engineering Technologies (iCoMET).
Gamma, E., Helm, R., and Johnson, R. E. (1994). Design Patterns. Elements of Reusable Object-Oriented Software. Addison-Wesley Longman, Amsterdam, 1st edition.
García del Valle, E. P., Lagunes García, G., Prieto Santamaría, L., Zanin, M., Menasalvas Ruiz, E., and Rodríguez-González, A. (2021). DisMaNET: A network-based tool to cross map disease vocabularies. Comp. Methods & Prog. Biomedicine, 207:106233.
Hassani-Pak, K., Singh, A., Brandizi, M., Hearnshaw, J., Parsons, J. D., Amberkar, S., Phillips, A. L., Doonan, J. H., and Rawlings, C. (2021). KnetMiner: a comprehensive approach for supporting evidence-based gene discovery and complex trait analysis across species. Plant Biotechnology Journal, 19(8):1670-1678.
Hühne, R., Kessler, V., Fürstberger, A., Kühlwein, S., Platzer, M., Sühnel, J., Lausser, L., and Kestler, H. (2018). 3D Network exploration and visualisation for lifespan data. BMC Bioinformatics, 19(390).
Jo, S., Park, B., Lee, S., and Kim, J. (2021). OLGAVis: On-Line Graph Analysis and Visualization for Bibliographic Information Network. Applied Sciences, 11(9).
Kerzner, E., Lex, A., Sigulinsky, C. L., Urness, T., Jones, B. W., Marc, R. E., and Meyer, M. (2017). Graffinity: Visualizing connectivity in large graphs. Computer Graphics Forum (EuroVis), 36(3):251-260.
Kumar, V. and Teo, E. A. L. E. (2021). Exploring the application of property graph model in visualizing cobie data. Journal of facilities management, 19(4):500-526.
Lanum, C. (2016). Visualizing Graph Data. Manning Publications, 1 edition.
Müller, R., Mahler, D., Hunger, M., Nerche, J., and Harrer, M. (2018). Towards an open source stack to create a unified data source for software analysis and visualization. In 2018 IEEEWorking Conference on Software Visualization (VISSOFT), pages 107-111.
Neo4j Inc. (2022). Neo4j Browser User Interface Guide. https://neo4j.com/developer/neo4j-browser/.
Partl, C., Gratzl, S., Streit, M., Wassermann, A. M., Pfister, H., Schmalstieg, D., and Lex, A. (2016). Pathfinder: Visual analysis of paths in graphs. Computer Graphics Forum (EuroVis ’16), 35(3):71-80.
Rahman, M. and Karim, R. (2016). Comparative study of different methods of social network analysis and visualization. In Int. Conf. Networking Systems & Security.
Rodrigues, J. F., Tong, H., Traina, A. J. M., Faloutsos, C., and Leskovec, J. (2015). Gmine: A system for scalable, interactive graph visualization and mining. In Proc. 32nd Int. Conf. on Very Large Data Bases, page 1195-1198.
Summer, G., Kelder, T., Ono, K., Radonjic, M., Heymans, S., and Demchak, B. (2015). cyNeo4j: connecting Neo4j and Cytoscape. Bioinformatics, 31(23):3868-3869.
Tallat, R., Amir Latif, R. M., Ali, G., Zaheer, A. N., Farhan, M., and Aslam Shah, S. U. (2019). Visualization and analytics of biological data by using different tools and techniques. In IEEE 16th Int. Bhurban Conf. Applied Sci. & Tech., pages 291-303.
Yokoyama, T. T., Okada, M., and Taniguchi, T. (2021). Panacea: Visual exploration system for analyzing trends in annual recruitment using time-varying graphs. PLOS ONE, 16(3):1-22.
Zahoránszky-Kohalmi, G., Sheils, T., and Oprea, T. (2020). SmartGraph: a Network Pharmacology Investigation Platform. Journal of Cheminformatics, 12(5).
Publicado
19/09/2022
Como Citar
LEMOS, Lucas C.; RALHA, Célia G..
Visualização 3D para Bancos de Dados Orientados a Grafos: Um Estudo de Caso Utilizando Redes Sociais. In: WORKSHOP DE TRABALHOS DE ALUNOS DA GRADUAÇÃO (WTAG) - SIMPÓSIO BRASILEIRO DE BANCO DE DADOS (SBBD), 37. , 2022, Búzios.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2022
.
p. 7-13.
DOI: https://doi.org/10.5753/sbbd_estendido.2022.21836.
