ReviewBioData mining2014
Applications of the MapReduce programming framework to clinical big data analysis: current landscape and future trends.
Review in BioData mining, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it, 147 citations in OpenAlex.
- Smart Wearable Systems for the Remote Monitoring of Selected Vascular Disorders of the Lower Extremity: A Systematic Review.International journal of environmental research and public health · 2022Pooled it
- From episodic imaging to real-time sensor monitoring: translational advances in assessing fracture healing dynamics.Journal of orthopaedic translation · 2026Review
- Big Data, Extracting Insights, Comprehension, and Analytics in Cardiology: An Overview.Journal of healthcare engineering · 2021Review
- MaRe: Processing Big Data with application containers on Apache Spark.GigaScience · 2020Article
- Literature on Wearable Technology for Connected Health: Scoping Review of Research Trends, Advances, and Barriers.Journal of medical Internet research · 2019Article
- Inference of Large-scale Time-delayed Gene Regulatory Network with Parallel MapReduce Cloud Platform.Scientific reports · 2018Article
- Optimized distributed systems achieve significant performance improvement on sorted merging of massive VCF files.GigaScience · 2018Article
- A Systematic Review on Healthcare Analytics: Application and Theoretical Perspective of Data Mining.Healthcare (Basel, Switzerland) · 2018Review
- K-mer clustering algorithm using a MapReduce framework: application to the parallelization of the Inchworm module of Trinity.BMC bioinformatics · 2017Article
- MapReduce Algorithms for Inferring Gene Regulatory Networks from Time-Series Microarray Data Using an Information-Theoretic Approach.BioMed research international · 2017Article
- Using Distributed Data over HBase in Big Data Analytics Platform for Clinical Services.Computational and mathematical methods in medicine · 2017Article
- Scalable Predictive Analysis in Critically Ill Patients Using a Visual Open Data Analysis Platform.PloS one · 2016Article
- Big Data Technologies: New Opportunities for Diabetes Management.Journal of diabetes science and technology · 2015Review
- Knowledge Extraction from MEDLINE by Combining Clustering with Natural Language Processing.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2015Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.