The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Big Data and cloud computing: innovation opportunities and challenges
Open accessAbstract
Big Data has emerged in the past few years as a new paradigm providing abundant data and opportunities to improve and/or enable research and decision-support applications with unprecedented value for digital earth applications including business, sciences and engineering. At the same time, Big Data presents challenges for digital earth to store, transport, process, mine and serve the data. Cloud computing provides fundamental support to address the challenges with shared computing resources including computing, storage, networking and analytical software; the application of these resources has fostered impressive Big Data advancements. This paper surveys the two frontiers – Big Data and cloud computing – and reviews the advantages and consequences of utilizing cloud computing to tackling Big Data in the digital earth and relevant science domains. From the aspects of a general introduction, sources, challenges, technology status and research opportunities, the following observations are offered: (i) cloud computing and Big Data enable science discoveries and application developments; (ii) cloud computing provides major solutions for Big Data; (iii) Big Data, spatiotemporal thinking and various application domains drive the advancement of cloud computing and relevant technologies with new requirements; (iv) intrinsic spatiotemporal principles of Big Data and geospatial sciences provide the source for finding technical and theoretical solutions to optimize cloud computing and processing Big Data; (v) open availability of Big Data and processing capability pose social challenges of geospatial significance and (vi) a weave of innovations is transforming Big Data into geospatial research, engineering and business values. This review introduces future innovations and a research agenda for cloud computing supporting the transformation of the volume, velocity, variety and veracity into values of Big Data for local to global digital earth science and applications.
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Citations
Cites
Business intelligence and analytics: from big data to big impact
Chen, H., Chiang, R. H. L., & Storey, V. C. (2012) · MIS Quarterly · doi:10.2307/41703503 · Cited by 6,250
Fog computing and its role in the internet of things
Bonomi, F., Milito, R., Zhu, J., & Addepalli, S. (2012) · Proceedings of the first edition of the MCC workshop on Mobile cloud computing · doi:10.1145/2342509.2342513 · Cited by 6,244
Time-series clustering – A decade review
Aghabozorgi, S., Seyed Shirkhorshidi, A., & Ying Wah, T. (2015) · Information Systems · doi:10.1016/j.is.2015.04.007 · Cited by 1,826 · cited 1×
“Large, high-dimensional spatiotemporal data cannot be managed by existing data reduction algorithms within a tolerable time frame and acceptable quality (Aghabozorgi, Seyed Shirkhorshidi, and Ying Wah 2015; García, Luengo, and Herrera 2015).”
BackgroundBig Data computing and clouds: trends and future directions
Assunção, M. D., Calheiros, R. N., Bianchi, S., Netto, M. A. S., & Buyya, R. (2015) · Journal of Parallel and Distributed Computing · doi:10.1016/j.jpdc.2014.08.003 · Cited by 901
Big Data: Survey, Technologies, Opportunities, and Challenges
Khan, N., Yaqoob, I., Hashem, I. A. T., Inayat, Z., Mahmoud Ali, W. K., Alam, M., Shiraz, M., & Gani, A. (2014) · The Scientific World Journal · doi:10.1155/2014/712826 · Cited by 547
Scheduling of big data applications on distributed cloud based on QoS parameters
Sandhu, R., & Sood, S. K. (2015) · Cluster Computing · doi:10.1007/s10586-014-0416-6 · Cited by 54
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Smart cities—a structured literature review
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Plant Phenotyping: Past, Present, and Future
Pieruschka, R., & Schurr, U. (2019) · Plant Phenomics · doi:10.34133/2019/7507131 · Cited by 341
Social-media data for urban sustainability
Ilieva, R. T., & McPhearson, T. (2018) · Nature Sustainability · doi:10.1038/s41893-018-0153-6 · Cited by 291
Udemba, E. N. (2020) · Science of The Total Environment · doi:10.1016/j.scitotenv.2020.139270 · Cited by 180
Future challenges and mitigation methods for high photovoltaic penetration: A survey
Olowu, T. O., Sundararajan, A., Moghaddami, M., & Sarwat, A. I. (2018) · Energies · doi:10.3390/en11071782 · Cited by 167
Metadata
- Authors
- Yang, C., Huang, Q., Li, Z., Liu, K., & Hu, F.
- Venue
- International Journal of Digital Earth
- Publisher
- Informa UK Limited
- Published
- 2016-11-03
- Volume
- 10
- Issue
- 1
- Pages
- 13-53
- ISSN
- 1753-8947 (ISSN-L)
- 1753-8955
- OpenAlex
- W2548753368
- Cited by
- 838
- References
- 6
- FWCI
- 139.47
- Type
- journal article
- Language
- en
- Open access
- hybrid
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.