The DOI matches its Crossref record; 76 of the 76 references it cites were found in a registry or an index.
The state of the art and taxonomy of big data analytics: view from new big data framework
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Citations
Cites
Data-intensive applications, challenges, techniques and technologies: A survey on Big Data
Philip Chen, C. L., & Zhang, C.-Y. (2014) · Information Sciences · doi:10.1016/j.ins.2014.01.015 · Cited by 3,047
Urban planning and building smart cities based on the Internet of Things using Big Data analytics
Rathore, M. M., Ahmad, A., Paul, A., & Rho, S. (2016) · Computer Networks · doi:10.1016/j.comnet.2015.12.023 · Cited by 932
Manogaran, G., Varatharajan, R., Lopez, D., Kumar, P. M., Sundarasekar, R., & Thota, C. (2018) · Future Generation Computer Systems · doi:10.1016/j.future.2017.10.045 · Cited by 512
Genuer, R., Poggi, J.-M., Tuleau-Malot, C., & Villa-Vialaneix, N. (2017) · Big Data Research · doi:10.1016/j.bdr.2017.07.003 · Cited by 367
On the use of MapReduce for imbalanced big data using Random Forest
del Río, S., López, V., Benítez, J. M., & Herrera, F. (2014) · Information Sciences · doi:10.1016/j.ins.2014.03.043 · Cited by 328
kNN-IS: An Iterative Spark-based design of the k-Nearest Neighbors classifier for big data
Maillo, J., Ramírez, S., Triguero, I., & Herrera, F. (2017) · Knowledge-Based Systems · doi:10.1016/j.knosys.2016.06.012 · Cited by 316
Towards felicitous decision making: An overview on challenges and trends of Big Data
Wang, H., Xu, Z., Fujita, H., & Liu, S. (2016) · Information Sciences · doi:10.1016/j.ins.2016.07.007 · Cited by 268
SCARFF : A scalable framework for streaming credit card fraud detection with spark
Carcillo, F., Dal Pozzolo, A., Le Borgne, Y.-A., Caelen, O., Mazzer, Y., & Bontempi, G. (2018) · Information Fusion · doi:10.1016/j.inffus.2017.09.005 · Cited by 267
Big Data Analytics framework for Peer-to-Peer Botnet detection using Random Forests
Singh, K., Guntuku, S. C., Thakur, A., & Hota, C. (2014) · Information Sciences · doi:10.1016/j.ins.2014.03.066 · Cited by 265
A novel social media competitive analytics framework with sentiment benchmarks
He, W., Wu, H., Yan, G., Akula, V., & Shen, J. (2015) · Information & Management · doi:10.1016/j.im.2015.04.006 · Cited by 234
Cited by
Strielkowski, W., Vlasov, A., Selivanov, K., Muraviev, K., & Shakhnov, V. (2023) · Energies · doi:10.3390/en16104025 · Cited by 237
Recent trends in computational intelligence for educational big data analysis
Ikegwu, A. C., Nweke, H. F., & Anikwe, C. V. (2024) · Iran Journal of Computer Science · doi:10.1007/s42044-023-00158-5 · Cited by 53
Metadata
- Authors
- Mohamed, A., Najafabadi, M. K., Wah, Y. B., Zaman, E. A. K., & Maskat, R.
- Venue
- Artificial Intelligence Review
- Published
- 2019-02-01
- Volume
- 53
- Issue
- 2
- Pages
- 989-1037
- ISSN
- 0269-2821 (ISSN-L)
- 1573-7462
- OpenAlex
- W2913024106
- Cited by
- 265
- References
- 76
- FWCI
- 29.19
- Type
- journal article
- Language
- en
- Open access
- closed
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- Mohamed, A., Najafabadi, M. K., Wah, Y. B., Zaman, E. A. K., & Maskat, R. (2020). The state of the art and taxonomy of big data analytics: view from new big data framework. Artificial Intelligence Review. https://doi.org/10.1007/s10462-019-09685-9
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
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.
