Document - A data model-independent approach to big research data integration

2019

Journal article Open Access

A data model-independent approach to big research data integration

Barralesi Lenzi V., Meghini C., Thanos C.

Computer Science Applications semantic web Library and Information Sciences big research data Information Systems data integration ontology

The paper addresses the data integration problem in the context of the scientific domain. The main characteristics of the big research data that make the traditional approach of data integration unfeasible are presented. Two new emerging practices, i.e. an exploratory approach to data seeking and an empiricist epistemological approach to knowledge creation, are discussed. Based on these considerations, we present a new paradigm of data integration and an application ontology that supports it. The ontology is based on five types of events and every event is extensionally modelled as an input/output operation on the involved data entity. The strong point of the ontology and of the whole approach to data integration is that no assumption is made on the data models in which the databases or the views are expressed. This provides a level of generality that successfully deals with the heterogeneity of the domain.

Source: International journal of metadata, semantics and ontologies (Print) 13 (2019): 330–345. doi:10.1504/IJMSO.2019.102680

Publisher: Inderscience Enterprises,, [Olney] , Regno Unito

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BibTeX entry

@article{oai:it.cnr:prodotti:415840,
	title = {A data model-independent approach to big research data integration},
	author = {Barralesi Lenzi V. and Meghini C. and Thanos C.},
	publisher = {Inderscience Enterprises,, [Olney] , Regno Unito},
	doi = {10.1504/ijmso.2019.102680 and 10.1504/ijmso.2019.10024347},
	journal = {International journal of metadata, semantics and ontologies (Print)},
	volume = {13},
	pages = {330–345},
	year = {2019}
}