The Metamodel of Heritage Preservation for Medical Big Data

Chaczko, Z; Gordon, LC; Bożejko, W

The Metamodel of Heritage Preservation for Medical Big Data

Chaczko, Z

Gordon, LC Bożejko, W

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Publication Type:: Conference Proceeding
Citation:: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2018, 10672 LNCS pp. 366 - 371
Issue Date:: 2018-01-01

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Field	Value	Language
dc.contributor.author	Chaczko, Z https://orcid.org/0000-0002-2816-7510	en_US
dc.contributor.author	Gordon, LC	en_US
dc.contributor.author	Bożejko, W	en_US
dc.date.issued	2018-01-01	en_US
dc.identifier.citation	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2018, 10672 LNCS pp. 366 - 371	en_US
dc.identifier.isbn	9783319747262	en_US
dc.identifier.issn	0302-9743	en_US
dc.identifier.uri	http://hdl.handle.net/10453/116970
dc.description.abstract	© 2018, Springer International Publishing AG. At present the real challenge of Digital Data Preservation concerns methods of keeping all important attributes of the data and preserving their originality. The key is to keep the living part of the data. It is the essence of the Heritage concept. The Heritage is about the concrete data the concept gives the interconnection to other aspects of the reality. Nowadays the physical value and the aspects of items complete the relevance of information. But the question is what is heritage and which parameters defining the artifact or the information as a heritage? The context and the interpretation of data is the answer. The heritage term is defining as the crucial and central part of the presented research. Big data analytics in healthcare is evolving into a promising field for providing insight from very large data sets and improving outcomes while reducing costs.	en_US
dc.relation.ispartof	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)	en_US
dc.relation.isbasedon	10.1007/978-3-319-74727-9_43	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	The Metamodel of Heritage Preservation for Medical Big Data	en_US
dc.type	Conference Proceeding
utslib.citation.volume	10672 LNCS	en_US
utslib.for	0806 Information Systems	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/DVC (Teaching and Learning)
pubs.organisational-group	/University of Technology Sydney/DVC (Teaching and Learning)/Connected Intelligence Centre
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
pubs.organisational-group	/University of Technology Sydney/Strength - INEXT - Innovation in IT Services and Applications
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	10672 LNCS	en_US

Abstract:

© 2018, Springer International Publishing AG. At present the real challenge of Digital Data Preservation concerns methods of keeping all important attributes of the data and preserving their originality. The key is to keep the living part of the data. It is the essence of the Heritage concept. The Heritage is about the concrete data the concept gives the interconnection to other aspects of the reality. Nowadays the physical value and the aspects of items complete the relevance of information. But the question is what is heritage and which parameters defining the artifact or the information as a heritage? The context and the interpretation of data is the answer. The heritage term is defining as the crucial and central part of the presented research. Big data analytics in healthcare is evolving into a promising field for providing insight from very large data sets and improving outcomes while reducing costs.

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http://hdl.handle.net/10453/116970