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Big Data: A Survey

Chen, Min, Mao, Shiwen, Liu, Yunhao·2014·Mobile Networks and Applications

DOI 10.1007/s11036-013-0489-0

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MapReduce: simplified data processing on large clusters
Dean, J., & Ghemawat, S. (2008) · Communications of the ACM · doi:10.1145/1327452.1327492 · Cited by 18,476
The anatomy of a large-scale hypertextual Web search engine
Brin, S., & Page, L. (1998) · Computer Networks and ISDN Systems · doi:10.1016/s0169-7552(98)00110-x · Cited by 16,004
An index to quantify an individual’s scientific research output
Hirsch, J. E. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0507655102 · Cited by 11,669
Foundations of statistical natural language processing
Manning, C. D., & Schütze, H. (1999) · Cited by 9,816
An Introduction to Multivariate Statistical Analysis
Muirhead, R. J., & Anderson, T. W. (1986) · Journal of Business and Economic Statistics · doi:10.2307/1391399 · Cited by 9,179
Top 10 algorithms in data mining
Wu, X., Kumar, V., Ross Quinlan, J., Ghosh, J., Yang, Q., Motoda, H., McLachlan, G. J., Ng, A., Liu, B., Yu, P. S., et al. (2008) · Knowledge and Information Systems · doi:10.1007/s10115-007-0114-2 · Cited by 5,667
Untitled work
(2011) · Cited by 5,203
Detecting influenza epidemics using search engine query data
Ginsberg, J., Mohebbi, M. H., Patel, R. S., Brammer, L., Smolinski, M. S., & Brilliant, L. (2009) · Nature · doi:10.1038/nature07634 · Cited by 4,295
Big data: a revolution that will transform how we live, work, and think
(2013) · Choice Reviews Online · doi:10.5860/choice.50-6804 · Cited by 3,657
Resilient distributed datasets: a fault-tolerant abstraction for in-memory cluster computing
Zaharia, M., Chowdhury, M., Das, T., Dave, A., Ma, J. H., McCauley, M., Franklin, M. J. M., Shenker, S. J., & Stoica, I. G. (2012) · Cited by 3,622

Show all 148 references

Research works printed but not linked: 3

Big data (2008). http://www.nature.com/news/specials/bigdata/index.html
Special online collection: dealing with big data (2011). http://www.sciencemag.org/site/special/data/
Sallam RL, Richardson J, Hagerty J, Hostmann B (2011) Magic quadrant for business intelligence platforms. CT, Gartner Group, Stamford

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Authors: Chen, M., Mao, S., & Liu, Y.
Affiliations: Huazhong University of Science and Technology (CN)
Auburn University (US)
Tsinghua University (CN)
Corresponding author: Chen, Min
ORCID: Chen, Min
Mao, Shiwen
Liu, Yunhao
Venue: Mobile Networks and Applications
Publisher: Springer Science and Business Media LLC
Published: 2014-01-22
Volume: 19
Issue: 2
Pages: 171-209
ISSN: 1383-469X (ISSN-L)
1572-8153
DOI: 10.1007/s11036-013-0489-0
OpenAlex: W2159128662
Cited by: 2,913
References: 148
Citations by year: 2026: 117 · 2025: 166 · 2024: 164 · 2023: 171 · 2022: 206 · 2021: 293 · 2020: 323 · 2019: 341 · 2018: 389 · 2017: 309 · 2016: 256 · 2015: 146 · 2014: 28
FWCI: 461.46
Type: journal article
Language: en
Open access: closed
Licence: www.springer.com/tdm (tdm)
Locations: Mobile Networks and Applications (publishedVersion)
Topics: Social Sciences › Decision Sciences › Information Systems and Management › Big Data Technologies and Applications
Physical Sciences › Computer Science › Information Systems › Cloud Computing and Resource Management
Physical Sciences › Computer Science › Information Systems › Big Data and Digital Economy
Keywords: big data, cloud computing, Internet of Things, data centers, Hadoop, data generation, data acquisition, data storage, data analysis, business management, social networking sites, medical applications, collective intelligence, smart grid

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