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Big Data: A Survey
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“In addition, the combination of datasets in the big data age needs to cope with a number of challenges listed in [20,61], for example efficient data representation, redundancy reduction and data compression, spurious correlations, and noise accumulation.”
From Opening up Smart Cities: Citizen-Centric Challenges and Opportunities from GIScience (2016) · cites this paper as [20]
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“These five characteristics help to define big data, building on earlier conceptualizations (Adrian, 2011; M. Chen, Mao, & Liu, 2014; Davenport, Barth, & Bean, 2012; Gantz & Reinsel, 2011; Hota, Upadhyaya, & Al-Karaki, 2015; Janssen & Kuk, 2016; Mayer-Schönberger & Cukier, 2013; P. Simon, 2013).”
Metadata
- 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
- Venue
- Mobile Networks and Applications
- Published
- 2014-01-22
- Volume
- 19
- Issue
- 2
- Pages
- 171-209
- ISSN
- 1383-469X (ISSN-L)
- 1572-8153
- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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