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Applications of the MapReduce programming framework to clinical big data analysis: current landscape and future
Emad A Mohammed1, Behrouz H Far1, Christopher Naugler2
1Department of Electrical and Computer Engineering, Schulich School of Engineering, University of Calgary, Calgary, AB, Canada.
Biodata Mining
|November 11, 2014
Summary
Massive clinical datasets require novel solutions like MapReduce and Hadoop for efficient, scalable analysis. These frameworks offer fault-tolerant storage and high-throughput processing for medical big data.
Area of Science:
- Health Informatics
- Computer Science
Background:
- Clinical settings generate massive datasets, challenging traditional data storage and analysis tools.
- Big data in healthcare necessitates advanced, scalable information and communication technology solutions.
- Novel approaches are required to handle large volumes of semi-structured and unstructured clinical data.
Purpose of the Study:
- To review applications of the MapReduce programming framework and Hadoop platform in clinical big data.
- To summarize state-of-the-art clinical big data analytics efforts.
- To identify enhancements for clinical big data analytics tools.
Main Methods:
- Utilizing the MapReduce programming framework for parallel data processing.
- Implementing Hadoop as an open-source platform for distributed big data analysis.
- Leveraging the Hadoop Distributed File System (HDFS) for data storage and access.
Main Results:
- MapReduce and Hadoop provide fault-tolerant storage through task replication and data chunk cloning.
- These frameworks enable high-throughput data processing via batch processing and HDFS.
- The use of MapReduce and Hadoop represents a significant advance in clinical big data processing.
Conclusions:
- MapReduce and Hadoop offer viable, efficient, and scalable solutions for clinical big data analysis.
- These technologies open new opportunities in medical health informatics and big data analytics.
- Further enhancement of clinical big data analytics tools is needed to maximize outcomes.
Keywords:
Big dataBioinformaticsClinical big data analysisClinical data analysisDistributed programmingHadoopMapReduceMore Related Videos
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