Towards Enabling the Analysis of Visual Exploration Processes through Interaction Provenance

• Lyncoln S. de Oliveira Universidade Federal do Rio de Janeiro http://orcid.org/0000-0002-0015-0709
• Gustavo Moreira Universidade de Illinois em Chicago
• Fábio Miranda Universidade de Illinois em Chicago
• Marcos Lage Universidade Federal Fluminense
• Daniel de Oliveira Universidade Federal Fluminense

DOI: https://doi.org/10.5753/sbbd.2025.247765

Resumo

The rapid growth of data has made accessing, integrating, and analyzing information increasingly challenging. While large-scale systems support processing and querying, interactive visualizations are essential for exploring complex datasets. Understanding how users gain insights from these visualizations requires capturing their interactions. Provenance data offers a natural solution, but current methods often fail to capture interaction-level provenance effectively. This paper presents an approach to capture and record user interaction provenance and integrate it with both prospective and retrospective provenance. We implement this approach in the Curio framework, which builds urban data visualization pipelines. Results demonstrate its effectiveness in capturing user behavior during visual exploration.

Palavras-chave: provenance, interaction, visualization

Referências

Ang, L.-M. and Seng, K. P. (2016). Big sensor data applications in urban environments. Big Data Research, 4:1–12.

Bavoil, L., Callahan, S., Crossno, P., Freire, J., et al. (2005). Vistrails: enabling interactive multiple-view visualizations. In IEEE Vis 2005, pages 135–142.

Biljecki, F. and Ito, K. (2021). Street view imagery in urban analytics and GIS: A review. Landscape and Urban Planning, 215:104217.

de Oliveira, W. M. et al. (2018). Provenance analytics for workflow-based computational experiments: A survey. ACM Comp. Surv., 51(3):53:1–53:25.

Fekete, J.-D. and Freire, J. (2020). Exploring reproducibility in visualization. IEEE Computer Graphics and Applications, 40(5):108–119.

Ferreira, N., Poco, J., et al. (2013). Visual exploration of big spatio-temporal urban data: A study of New York City taxi trips. IEEE TVCG, 19(12):2149–2158.

Heer, J., Bostock, M., and Ogievetsky, V. (2010). A tour through the visualization zoo. ACM Queue, 8(5):20–30.

Herschel, M., Diestelkämper, R., and Ben Lahmar, H. (2017). A survey on provenance: What for? what form? what from? VLDB J., 26(6):881–906.

Loibl, W., Vuckovic, M., Etminan, G., Ratheiser, M., Tschannett, S., and Österreicher, D. (2021). Effects of densification on urban microclimate—a case study for the city of vienna. Atmosphere, 12(4).

Miranda, F., Hosseini, M., et al. (2020). Urban Mosaic: Visual exploration of streetscapes using large-scale image data. CHI ’20, page 1–15, New York, NY, USA.

Miranda, F., Ortner, T., Moreira, G., et al. (2024). The state of the art in visual analytics for 3D urban data. Computer Graphics Forum, 43(3):e15112.

Moreira, G., Hosseini, M., et al. (2024). The Urban Toolkit: A grammar-based framework for urban visual analytics. IEEE TVCG, 30(1):1402–1412.

Moreira, G., Hosseini, M., et al. (2025). Curio: A dataflow-based framework for collaborative urban visual analytics. IEEE TVCG, 31(1):1224–1234.

Oliveira, L. F., Oliveira, D., and Frota, Y. (2023). Defining routes for emergency response from climate events: a data-oriented approach. IEEE Latin Am. Trans., 21(10):1064–1072.

Psallidas, F. and Wu, E. (2018). Provenance for interactive visualizations. In Proceedings of the Workshop on Human-In-the-Loop Data Analytics, HILDA ’18, New York, NY, USA.

Satyanarayan, A., Moritz, D., Wongsuphasawat, K., and Heer, J. (2017). Vega-lite: A grammar of interactive graphics. IEEE Trans. Visualization & Comp. Graphics (Proc. InfoVis).

Sá, B. et al. (2021). Polroute-ds: um dataset de dados criminais para geração de rotas de patrulhamento policial. In DSW’21, pages 117–127. SBC.

Walchshofer, C. et al. (2021). Provectories: Embedding-based analysis of interaction provenance data. IEEE TVCG).

Yu, B. and Silva, C. T. (2017). Visflow - web-based visualization framework for tabular data with a subset flow model. IEEE Transactions on Visualization and Computer Graphics, 23(1):251–260.