Extensible Unifying Data Model Design for Data Integration in FAIR Data Infrastructures View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2019-07-04

AUTHORS

Sergey Stupnikov , Leonid Kalinichenko

ABSTRACT

According to the Open Science paradigm data sources are to be concentrated within research data infrastructures intended to support the whole cycle of data management and processing. FAIR data management and stewardship principles that had being developed and announced recently state that data within a data infrastructure have to be findable, accessible, interoperable and reusable. Note that data sources can be quite heterogeneous and represented using very different data models. Variety of data models includes traditional relational model and its object-relational extensions, array and graph-based models, semantic models like RDF and OWL, models for semi-structured data like NoSQL, XML, JSON and so on. This particular paper overviews data model unification techniques considered as a formal basis for (meta)data interoperability, integration and reuse within FAIR data infrastructures. These techniques are intended to deal with heterogeneity of data models and their data manipulation languages used to represent data and provide access to data in data sources. General principles of data model unification, languages and formal methods required, stages of data model unification are considered and illustrated by examples. Application of the techniques for data integration within FAIR data infrastructures is discussed. More... »

PAGES

17-36

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-030-23584-0_2

DOI

http://dx.doi.org/10.1007/978-3-030-23584-0_2

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1117733078


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