Profiling for Confidence: Debugging Relationships among Urban Spatio-Temporal Datasets

• Laís M. A. Rocha UFMG
• Mirella M. Moro UFMG
• Juliana Freire NYU

DOI: https://doi.org/10.5753/ctd.2020.11375

Resumo

We aim to help users identify potential issues in spatio-temporal data and thus gain trust in the results they derive from such data -- a crucial benefit in the era of data science and big data. We propose a framework for profiling spatio-temporal relationships that automatically identifies data slices that deviate from what is expected, which can be further analyzed for quality issues and/or potential effects on analysis' results. We describe the profiling methodology and present cases studies using real urban datasets, then emphasizing the need for spatio-temporal profiling to build trust on data analysis' results.

 

Palavras-chave: Urban Data, Complex Relationships, Profiling

Referências

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Rocha, L. M., Bessa, A., Chirigati, F., OFriel, E., Moro, M. M., and Freire, J. (2019). Understanding spatio-temporal urban processes. In IEEE Big Data, pages 563-572.