It is well-known that Artificial Intelligence (AI), and in particular Machine Learning (ML), is not effective without good data preparation, as also pointed out by the recent wave of data-centric AI. Data preparation is the process of gathering, transforming and cleaning raw data prior to processing and analysis. Since nowadays data often reside in distributed and heterogeneous data sources, the first activity of data preparation requires collecting data from suitable data sources and data services, often distributed and heterogeneous. It is thus essential that providers describe their data services in a way to make them compliant with the FAIR guiding principles, i.e., make them automatically Findable, Accessible, Interoperable, and Reusable (FAIR). The notion of data abstraction has been introduced exactly to meet this need. Abstraction is a kind of reverse engineering task that automatically provides a semantic characterization of a data service made available by a provider. The goal of this paper is to review the results obtained so far in data abstraction, by presenting the formal framework for its definition, reporting about the decidability and complexity of the main theoretical problems concerning abstraction, and discuss open issues and interesting directions for future research.

A review of data abstraction / Cima, G.; Console, M.; Lenzerini, M.; Poggi, A.. - In: FRONTIERS IN ARTIFICIAL INTELLIGENCE. - ISSN 2624-8212. - 6:(2023). [10.3389/frai.2023.1085754]

A review of data abstraction

Cima G.
;
Console M.
;
Lenzerini M.
;
Poggi A.
2023

Abstract

It is well-known that Artificial Intelligence (AI), and in particular Machine Learning (ML), is not effective without good data preparation, as also pointed out by the recent wave of data-centric AI. Data preparation is the process of gathering, transforming and cleaning raw data prior to processing and analysis. Since nowadays data often reside in distributed and heterogeneous data sources, the first activity of data preparation requires collecting data from suitable data sources and data services, often distributed and heterogeneous. It is thus essential that providers describe their data services in a way to make them compliant with the FAIR guiding principles, i.e., make them automatically Findable, Accessible, Interoperable, and Reusable (FAIR). The notion of data abstraction has been introduced exactly to meet this need. Abstraction is a kind of reverse engineering task that automatically provides a semantic characterization of a data service made available by a provider. The goal of this paper is to review the results obtained so far in data abstraction, by presenting the formal framework for its definition, reporting about the decidability and complexity of the main theoretical problems concerning abstraction, and discuss open issues and interesting directions for future research.
2023
abstraction; automated reasoning; data integration; data preparation; knowledge representation
01 Pubblicazione su rivista::01a Articolo in rivista
A review of data abstraction / Cima, G.; Console, M.; Lenzerini, M.; Poggi, A.. - In: FRONTIERS IN ARTIFICIAL INTELLIGENCE. - ISSN 2624-8212. - 6:(2023). [10.3389/frai.2023.1085754]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1691314
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