NDLI: Provenance Support for Biomedical Big Data Analytics

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Provenance Support for Biomedical Big Data Analytics

Provenance Support for Biomedical Big Data Analytics

Content Provider	ACM Digital Library
Author	McClatchey, Richard Branson, Andrew Shamdasani, Jetendr
Abstract	One essential requirement for supporting analytics for Big Medical Data systems is the provision of a suitable level of traceability to data or processes ('Items') in large volumes of data. Systems should be designed from the outset to support usage of such Items across the spectrum of medical use and over time in order to promote traceability, to simplify maintenance and to assist analytics. The philosophy proposed in this paper is to design medical data systems using a 'description-driven' approach in which meta-data and the description of medical items are saved alongside the data, simplifying item re-use over time and thereby enabling the traceability of these items over time and their use in analytics. Details are given of a big data system in neuroimaging to demonstrate aspects of provenance data capture, collaborative analysis and longitudinal information traceability. Evidence is presented that the description-driven approach leads to simplicity of design and ease of maintenance following the adoption of a unified approach to Item management.
Starting Page	386
Ending Page	391
Page Count	6
File Format	PDF
ISBN	9781450341189
DOI	10.1145/2938503.2938540
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2016-07-11
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Traceability Description-driven systems Big data Provenance data Medical analytics
Content Type	Text
Resource Type	Article

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