A RDF-based semantic schema mapping transformation system for localized data integration

Cheong, Chi Po; Chatwin, Chris; Young, Rupert

doi:10.1109/icasid.2009.5276919

Cited by 2 publications

(1 citation statement)

References 6 publications

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“…[15] is another mapping effort that uses a set of well-defined rules, like [14], to map relational schema metadata into an equivalent RDFS ontology which is then written into an RDF/XML file. Several endeavors [16][17][18][19][20] are underway in the data integration arena as well that b http://www.w3.org/TR/rdf-schema/ Update-Enabled Triplification of Relational Data into Virtual RDF Stores 427 aim to present a semantically unified RDF model derived from multiple underlying heterogeneous databases.…”

Section: Related Workmentioning

confidence: 99%

Update-Enabled Triplification of Relational Data Into Virtual RDF Stores

Ramanujam

Khadilkar

Khan

et al. 2010

Int. J. Semantic Computing

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The current buzzword in the Internet community is the Semantic Web initiative proposed by the W3C to yield a Web that is more flexible and self-adapting. However, for the Semantic Web initiative to become a reality, heterogeneous data sources need to be integrated in order to enable access to them in a homogeneous manner. Since a vast majority of data currently resides in relational databases, integrating relational data sources with semantic web technologies is at the top of the list of activities required to realize the semantic web vision. Several efforts exist that publish relational data as Resource Description Framework (RDF) triples; however almost all current work in this arena is uni-directional, presenting data from an underlying relational database into a corresponding virtual RDF store in a read-only manner. An enhancement over previous relational-to-RDF bridging work in the form of bi-directionality support is presented in this paper. The bi-directional bridge proposed here allows RDF data updates specified as triples to be propagated back into the underlying relational database as tuples. Towards this end, we present various algorithms to translate the triples to be updated/inserted/deleted into equivalent relational attributes/tuples whenever possible. Particular emphasis is laid, in this paper, on the translation and update propagation process for triples containing blank nodes and reification nodes, and a platform enhanced with our algorithms, called D2RQ++, through which bi-directional translation can be achieved, is presented.

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Section: Related Workmentioning

confidence: 99%

Update-Enabled Triplification of Relational Data Into Virtual RDF Stores

Ramanujam

Khadilkar

Khan

et al. 2010

Int. J. Semantic Computing

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Towards a 'Big' Health Data Analytics Platform

Cha

Abusharekh

2015

2015 IEEE First International Conference on Big Data Computing Service and Applications

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Health is generating large volumes of data that can provide invaluable insights into clinical and operational aspects of healthcare delivery. There is a general lack of specialized and integrated health data analytics platforms that offer technical methods to support the entire health data analysis pipeline-i.e. health data selection, integration, analysis, visualization and sharing. This paper proposes the technical architecture of a health data analytics platform that offers a technical solution for analyzing 'big' health data originating from multiple sources with heterogeneous terminologies and schemas. A key aspect of the architecture is data standardization, where we have used SNOMED-CT as a terminology standard to standardize health data from multiple sources. We offer a single step health data integration solution where users can select the data sources and the data elements from multiple sources, and our platform performs the data standardization and data integration to prepare an integrated dataset. We present a case study involving large volumes of laboratory data that is integrated and analyzed using our platform.

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A RDF-based semantic schema mapping transformation system for localized data integration

Cited by 2 publications

References 6 publications

Update-Enabled Triplification of Relational Data Into Virtual RDF Stores

Update-Enabled Triplification of Relational Data Into Virtual RDF Stores

Towards a 'Big' Health Data Analytics Platform

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