Profiling for Confidence: Debugging Relationships among Urban Spatio-Temporal Datasets - Rocha, Laís M. A.; Moro, Mirella M.; Freire, Juliana (Anais do Concurso de Teses e Dissertações da SBC (CTD-SBC); 2020: Anais do XXXIII Concurso de Teses e Dissertações, 2020)
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.