Dove e quando

Today data is a mass as uncountable as dust, and data surrounds us like dust, even lovely structured data. The nature of such data is varied and not always clear and explicit. Even in the case of well-structured data, extracting and augmenting knowledge is crucial to solve data science goals, along with the development of technological solutions satisfying non-discriminating requirements. The problems raised by such a mass of data vary from discovery and integration to ethics and fairness guarantees. In this talk, we will focus on two aspects of structured data manipulation: table discovery in massive data lakes (without a global schema) and the generation of fair results through ethic-by-design query processing techniques (knowing the schema). The approaches we will describe could be applied to any tabular structure (e.g. dataframes) and are not limited to tables coming from databases. Bio: Federico Dassereto is a second-year Ph.D. student in Computer Science, in the Data Management and Analysis Group (DAMA). He obtained a research fellowship at DIBRIS from February 2019 to October 2019. His research interests include (open) data integration and embeddings for data management. In particular, his focus is on table discovery in massive data lakes. He also investigated geospatial data with a Erasmus+ grant at University College Dublin (Ireland). Chiara Accinelli received her BSc in Statistica Matematica e Trattamento Informatico dei Dati (SMID), in 2016, and her MSc in Computer Science, in 2018, both at the University of Genoa. Then, she obtained a research fellowship at DIBRIS from February 2019 to October 2019. She is now a second year Ph.D. student in Computer Science, in the Data Management and Analysis Group (DAMA). Her research interests include the development of data management processing techniques satisfying non-discriminating requirements aiming to obtain fair results.

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