MODELING THE DASHBOARD PROVENANCE

Authors

  • Johne Jarske
  • Jorge Rady
  • Lucia V. L. Filgueiras
  • Leandro M. Velloso
  • Tania L. Santos

DOI:

https://doi.org/10.56238/revgeov17n4-075

Keywords:

Provenance Model, Dashboard, Visualization, User Experience

Abstract

Organizations of all kinds, whether public or private, profit-driven or non-profit, and across various industries and sectors, rely on dashboards for effective data visualization. However, the reliability and efficacy of these dashboards rely on the quality of the visuals and data they present. Studies show that fewer than a quarter of dashboards provide information about their sources, which is just one of the expected pieces of metadata when provenance is seriously considered. Provenance is a record that describes people, organizations, entities, and activities that had a role in the production, influence, or delivery of a piece of data or an object. This paper aims to provide a provenance representation model that enables standardization, modeling, generation, capture, and visualization, specifically designed for dashboards and their visual and data components. The proposed model will provide a comprehensive set of provenance metadata, enabling users to evaluate the quality, consistency, and reliability of the information presented on dashboards. This will provide a clear, precise understanding of the context in which a specific dashboard was developed, ultimately leading to better decision-making.

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References

Sarikaya, A., Correll, M., Bartram, L., Tory, M., & Fisher, D. (2019). What do we talk about when we talk about dashboards? IEEE Transactions on Visualization and Computer Graphics, 25, 682–692. https://doi.org/10.1109/TVCG.2018.2864903

Shankar, K., Jeng, W., Thomer, A., Weber, N., & Yoon, A. (2021). Data curation as collective action during COVID-19. Journal of the Association for Information Science and Technology, 72, 280–284. https://doi.org/10.1002/ASI.24406

Moreau, L., & Groth, P. (2013). Provenance: An introduction to PROV (Vol. 3, pp. 1–131). https://doi.org/10.2200/S00528ED1V01Y201308WBE007

Centers for Disease Control and Prevention. (2022). COVID data tracker. https://covid.cdc.gov/covid-data-tracker

Johns Hopkins University. (2022). Coronavirus resource center. https://coronavirus.jhu.edu/

Florida Department of Health. (2022). Florida COVID action. https://floridacovidaction.com/

Tompkins, A., & Humphries, L. (2020). Provenance research today: Principles, practice, problems (1st ed.). Lund Humphries.

Fuhrmeister, C., & Hopp, M. (2019). Rethinking provenance research. Getty Research Journal, 11(1), 213–231. https://doi.org/10.1086/702755

Rossenova, L., de Wild, K., & Espenschied, D. (2019). Provenance for internet art using the W3C PROV data model. In Proceedings of the 16th International Conference on Digital Preservation (iPRES 2019) (pp. 16–20).

Gramlich, J. (2017). Reflections on provenance research: Values – politics – art markets. Journal for Art Market Studies, 1(2), 322–327. https://doi.org/10.23690/jams.v1i2.15

Cheney, J., Chong, S., Foster, N., Seltzer, M., & Vansummeren, S. (2009). Provenance: A future history. In Proceedings of OOPSLA (pp. 957–964). https://doi.org/10.1145/1639950.1640064

Moreau, L., & Foster, I. (2006). Provenance and annotation of data: International Provenance and Annotation Workshop (IPAW 2006) (Vol. 1). Springer.

Provenance Incubator Group. (2010). Provenance dimensions. https://www.w3.org/2005/Incubator/prov/wiki/Provenance_Dimensions

Zhang, M., Jiang, L., Zhao, J., Yue, P., & Zhang, X. (2020). Coupling OGC WPS and W3C PROV for provenance-aware geoprocessing workflows. Computers & Geosciences, 138, 104419. https://doi.org/10.1016/j.cageo.2020.104419

Groth, P., & Moreau, L. (2013). PROV-overview – an overview of the PROV family of documents. W3C Working Group. https://www.w3.org/2005/Incubator/prov/wiki/Use_Case_Template

Zhang, Y., Sun, Y., Gaggiano, J. D., Kumar, N., Andris, C., & Parker, A. G. (2022). Visualization design practices in a crisis: Behind the scenes with COVID-19 dashboard creators. https://doi.org/10.48550/arXiv.2207.12829

Matheus, R., Janssen, M., & Maheshwari, D. (2020). Data science empowering the public: Data-driven dashboards for transparent and accountable decision-making in smart cities. Government Information Quarterly, 37, 101284. https://doi.org/10.1016/J.GIQ.2018.01.006

Ivanković, D., et al. (2021). Features constituting actionable COVID-19 dashboards. Journal of Medical Internet Research, 23. https://doi.org/10.2196/25682

Jarske, J. M., Rady, J., Filgueiras, L. V. L., Velloso, L. M., & Santos, T. L. (2022). Data provenance visualization in Brazilian public health dashboards. TREX 2022 Workshop on Trust and Expertise in Visualization.

Brügger, N., & Milligan, I. (2018). The SAGE handbook of web history (1st ed.). SAGE Publications.

Moreau, L. (2010). The foundations for provenance on the web. Foundations and Trends in Web Science, 2, 99–241. https://doi.org/10.1561/1800000010

HL7 Community. (2022). HL7 FHIR specification: Resource provenance – content. https://www.hl7.org/fhir/provenance.html

dos Santos, R. R., Santos, M. T. P., & Ciferri, R. R. (2023). ProvOER model: A provenance model for open educational resources. Heliyon, 9, e13311. https://doi.org/10.1016/J.HELIYON.2023.E13311

Ragan, E. D., Endert, A., Sanyal, J., & Chen, J. (2016). Characterizing provenance in visualization and data analysis. IEEE Transactions on Visualization and Computer Graphics, 22, 31–40. https://doi.org/10.1109/TVCG.2015.2467551

RFC 4180. (2005). Common Format and MIME Type for CSV Files. https://www.rfc-editor.org/rfc/rfc4180

RFC 8259. (2017). The JavaScript Object Notation (JSON) data interchange format. https://tools.ietf.org/html/rfc8259

RFC 7946. (2016). The GeoJSON Format. https://tools.ietf.org/html/rfc7946

Ministério da Saúde. (2023). COVID-19 no Brasil. https://infoms.saude.gov.br/extensions/covid-19_html

Moreau, L., Missier, P., et al. (2013). PROV-DM: The PROV data model for provenance. https://www.w3.org/TR/prov-dm/

Werner, B., & Moreau, L. (2020). ProvViz: An intuitive PROV editor and visualizer. In International Provenance and Annotation Workshop (pp. 231–236). Springer.

Zhai, J., Chen, H., & Yuan, C. (2017). Provenance metadata of open government data based on PROV-JSON. In ACM International Conference Proceedings Series (pp. 594–595). https://doi.org/10.1145/3085228.3085229

Huynh, T. D., Jewell, M. O., Keshavarz, A. S., Michaelides, D. T., Yang, H., & Moreau, L. (2013). The PROV-JSON serialization. https://www.w3.org/Submission/prov-json/#introduction

Yazici, I. M., Karabulut, E., & Aktas, M. S. (2018). A data provenance visualization approach. In 2018 14th International Conference on Semantics, Knowledge and Grids (SKG) (pp. 84–91). IEEE. https://doi.org/10.1109/SKG.2018.00019

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Published

2026-04-19

How to Cite

Jarske, J., Rady, J., Filgueiras, L. V. L., Velloso, L. M., & Santos, T. L. (2026). MODELING THE DASHBOARD PROVENANCE. Revista De Geopolítica, 17(4), e2143. https://doi.org/10.56238/revgeov17n4-075