Enabling Big Geoscience Data Analytics with a Cloud-Based, MapReduce-Enabled and Service-Oriented Workflow Framework

Geoscience observations and model simulations are generating vast amounts of multi-dimensional data. Effectively analyzing these data are essential for geoscience studies. However, the tasks are challenging for geoscientists because processing the massive amount of data is both computing and data in...

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Vydané v:PloS one Ročník 10; číslo 3; s. e0116781
Hlavní autori: Li, Zhenlong, Yang, Chaowei, Jin, Baoxuan, Yu, Manzhu, Liu, Kai, Sun, Min, Zhan, Matthew
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States Public Library of Science 05.03.2015
Public Library of Science (PLoS)
Predmet:
Algorithms
Analysis
Analytics
Big Data
Bioinformatics
Climate change
Cloud Computing
Computational Biology - methods
Computer simulation
Computers
Data analysis
Data processing
Dimensional analysis
Earth science
Earth Sciences
Experiments
Internet
Internet service providers
Mathematical analysis
Multidimensional data
Natural disasters
Parallel processing
Prototype tests
Sensors
Service oriented architecture
Simulation
United States
Variables
Workflow
United States
United States > US
ISSN:1932-6203, 1932-6203
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Popis
Shrnutí:Geoscience observations and model simulations are generating vast amounts of multi-dimensional data. Effectively analyzing these data are essential for geoscience studies. However, the tasks are challenging for geoscientists because processing the massive amount of data is both computing and data intensive in that data analytics requires complex procedures and multiple tools. To tackle these challenges, a scientific workflow framework is proposed for big geoscience data analytics. In this framework techniques are proposed by leveraging cloud computing, MapReduce, and Service Oriented Architecture (SOA). Specifically, HBase is adopted for storing and managing big geoscience data across distributed computers. MapReduce-based algorithm framework is developed to support parallel processing of geoscience data. And service-oriented workflow architecture is built for supporting on-demand complex data analytics in the cloud environment. A proof-of-concept prototype tests the performance of the framework. Results show that this innovative framework significantly improves the efficiency of big geoscience data analytics by reducing the data processing time as well as simplifying data analytical procedures for geoscientists.
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Competing Interests: The authors have declared that no competing interests exist.
Conceived and designed the experiments: CY ZL BJ. Performed the experiments: ZL KL MY MZ. Analyzed the data: ZL CY BJ KL MY. Contributed reagents/materials/analysis tools: ZL CY BJ MS KL. Wrote the paper: ZL CY MY BJ.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0116781