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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Applications of the MapReduce programming framework to clinical big data analysis: current landscape and future trends
|
|---|---|
| Published in |
BioData Mining, October 2014
|
| DOI | 10.1186/1756-0381-7-22 |
| Pubmed ID | |
| Authors |
Emad A Mohammed, Behrouz H Far, Christopher Naugler |
| Abstract |
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. THE MAPREDUCE PROGRAMMING FRAMEWORK USES TWO TASKS COMMON IN FUNCTIONAL PROGRAMMING: Map and Reduce. MapReduce is a new parallel processing framework and Hadoop is its open-source implementation on a single computing node or on clusters. Compared with existing parallel processing paradigms (e.g. grid computing and graphical processing unit (GPU)), MapReduce and Hadoop have two advantages: 1) fault-tolerant storage resulting in reliable data processing by replicating the computing tasks, and cloning the data chunks on different computing nodes across the computing cluster; 2) high-throughput data processing via a batch processing framework and the Hadoop distributed file system (HDFS). Data are stored in the HDFS and made available to the slave nodes for computation. In this paper, we review the existing applications of the MapReduce programming framework and its implementation platform Hadoop in clinical big data and related medical health informatics fields. The usage of MapReduce and Hadoop on a distributed system represents a significant advance in clinical big data processing and utilization, and opens up new opportunities in the emerging era of big data analytics. The objective of this paper is to summarize the state-of-the-art efforts in clinical big data analytics and highlight what might be needed to enhance the outcomes of clinical big data analytics tools. This paper is concluded by summarizing the potential usage of the MapReduce programming framework and Hadoop platform to process huge volumes of clinical data in medical health informatics related fields. |
X Demographics
The data shown below were collected from the profiles of 10 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 3 | 30% |
| United Kingdom | 2 | 20% |
| France | 1 | 10% |
| Italy | 1 | 10% |
| Spain | 1 | 10% |
| Unknown | 2 | 20% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 5 | 50% |
| Members of the public | 5 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 274 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 3 | 1% |
| Australia | 2 | <1% |
| France | 1 | <1% |
| Chile | 1 | <1% |
| India | 1 | <1% |
| Brazil | 1 | <1% |
| Belgium | 1 | <1% |
| Canada | 1 | <1% |
| Unknown | 263 | 96% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 52 | 19% |
| Student > Master | 51 | 19% |
| Researcher | 33 | 12% |
| Student > Bachelor | 20 | 7% |
| Student > Postgraduate | 16 | 6% |
| Other | 58 | 21% |
| Unknown | 44 | 16% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 105 | 38% |
| Engineering | 23 | 8% |
| Agricultural and Biological Sciences | 21 | 8% |
| Medicine and Dentistry | 21 | 8% |
| Biochemistry, Genetics and Molecular Biology | 10 | 4% |
| Other | 41 | 15% |
| Unknown | 53 | 19% |
Attention Score in Context
This research output has an Altmetric Attention Score of 8. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 12 June 2015.
All research outputs
#4,761,450
of 25,187,238 outputs
Outputs from BioData Mining
#99
of 320 outputs
Outputs of similar age
#50,658
of 267,367 outputs
Outputs of similar age from BioData Mining
#4
of 8 outputs
Altmetric has tracked 25,187,238 research outputs across all sources so far. Compared to these this one has done well and is in the 81st percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 320 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 7.5. This one has gotten more attention than average, scoring higher than 69% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 267,367 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 81% of its contemporaries.
We're also able to compare this research output to 8 others from the same source and published within six weeks on either side of this one. This one has scored higher than 4 of them.