Applications of the MapReduce programming framework to clinical big data analysis: current landscape and future trends

被引：0
|
作者：
Emad A Mohammed
Behrouz H Far
Christopher Naugler
机构：
[1] Schulich School of Engineering,Department of Electrical and Computer Engineering
[2] University of Calgary,Department of Pathology and Laboratory Medicine
[3] University of Calgary and Calgary Laboratory Services,undefined
来源：
BioData Mining | / 7卷
关键词：
MapReduce; Hadoop; Big data; Clinical big data analysis; Clinical data analysis; Bioinformatics; Distributed programming;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
The emergence of massive datasets in a clinical setting presents both challenges and opportunities in data storage and analysis. This so called “big data” challenges traditional analytic tools and will increasingly require novel solutions adapted from other fields. Advances in information and communication technology present the most viable solutions to big data analysis in terms of efficiency and scalability. It is vital those big data solutions are multithreaded and that data access approaches be precisely tailored to large volumes of semi-structured/unstructured data.
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