The DOI matches its Crossref record; 18 of the 18 references it cites were found in a registry or an index.
Towards felicitous decision making: An overview on challenges and trends of Big Data
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Citations
Cites
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
The Elements of Statistical Learning
Hastie, T., Tibshirani, R., & Friedman, J. (2009) · Springer Series in Statistics · doi:10.1007/978-0-387-84858-7 · Cited by 25,970
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
The Parable of Google Flu: Traps in Big Data Analysis
Lazer, D., Kennedy, R., King, G., & Vespignani, A. (2014) · Science · doi:10.1126/science.1248506 · Cited by 2,523 · cited 1×
“[82] stated that human analysts are necessary to remain in the loop in certain scenarios.”
BackgroundWu, X., Zhu, X., Wu, G.-Q., & Ding, W. (2014) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2013.109 · Cited by 2,410
Predicting the future with social media
Asur, S., & Huberman, B. A. (2010) · 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology · doi:10.1109/wi-iat.2010.63 · Cited by 2,081
Mass-spectrometry-based draft of the human proteome
Wilhelm, M., Schlegl, J., Hahne, H., Gholami, A. M., Lieberenz, M., Savitski, M. M., Ziegler, E., Butzmann, L., Gessulat, S., Marx, H., et al. (2014) · Nature · doi:10.1038/nature13319 · Cited by 1,820
Data-Driven Intelligent Transportation Systems: A Survey
Zhang, J., Wang, F.-Y., Wang, K., Lin, W.-H., Xu, X., & Chen, C. (2011) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2011.2158001 · Cited by 1,817
The World’s Technological Capacity to Store, Communicate, and Compute Information
Hilbert, M., & López, P. (2011) · Science · doi:10.1126/science.1200970 · Cited by 1,643
Duncan, L. E., & Keller, M. C. (2011) · American Journal of Psychiatry · doi:10.1176/appi.ajp.2011.11020191 · Cited by 1,032
Cited by
The state of the art and taxonomy of big data analytics: view from new big data framework
Mohamed, A., Najafabadi, M. K., Wah, Y. B., Zaman, E. A. K., & Maskat, R. (2020) · Artificial Intelligence Review · doi:10.1007/s10462-019-09685-9 · Cited by 265
Metadata
- Authors
- Wang, H., Xu, Z., Fujita, H., & Liu, S.
- Venue
- Information Sciences
- Publisher
- Elsevier BV
- Published
- 2016-11
- Volume
- 367-368
- Pages
- 747-765
- ISSN
- 0020-0255 (ISSN-L)
- OpenAlex
- W2468725466
- Cited by
- 268
- References
- 18
- FWCI
- 61.51
- Type
- journal article
- Language
- en
- Open access
- closed
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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.
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- CoChat. (2026). Citation check: Towards felicitous decision making: An overview on challenges and trends of Big Data. https://app.cochat.ai/papers/10.1016/j.ins.2016.07.007
- The paper
- Wang, H., Xu, Z., Fujita, H., & Liu, S. (2016). Towards felicitous decision making: An overview on challenges and trends of Big Data. Information Sciences. https://doi.org/10.1016/j.ins.2016.07.007
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.
