The DOI matches its Crossref record; 50 of the 53 references it cites were found in a registry or an index.
Data mining with big data
Abstract
Big Data concern large-volume, complex, growing data sets with multiple, autonomous sources. With the fast development of networking, data storage, and the data collection capacity, Big Data are now rapidly expanding in all science and engineering domains, including physical, biological and biomedical sciences. This paper presents a HACE theorem that characterizes the features of the Big Data revolution, and proposes a Big Data processing model, from the data mining perspective. This data-driven model involves demand-driven aggregation of information sources, mining and analysis, user interest modeling, and security and privacy considerations. We analyze the challenging issues in the data-driven model and also in the Big Data revolution.
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“However, traditional data management approaches are capable neither of managing such a large and diversified amount of data nor of handling its effective growth and maintenance as the volume and velocity of relevant data increase [4; 27; 73].”
From A Big Data Analytics Method for Tourist Behaviour Analysis (2017) · cites this paper as [73]
Citations
Cites
Twitter mood predicts the stock market
Bollen, J., Mao, H., & Zeng, X. (2011) · Journal of Computational Science · doi:10.1016/j.jocs.2010.12.007 · Cited by 5,206
Network Analysis in the Social Sciences
Borgatti, S. P., Mehra, A., Brass, D. J., & Labianca, G. (2009) · Science · doi:10.1126/science.1165821 · Cited by 4,280
The spread of behavior in an online social network experiment
Centola, D. (2010) · Science · doi:10.1126/science.1185231 · Cited by 2,692
Rajaraman, A., & Ullman, J. D. (2011) · Cambridge University Press eBooks · doi:10.1017/cbo9781139058452 · Cited by 2,307
How to generate and exchange secrets
Yao, A. (1986) · 27th Annual Symposium on Foundations of Computer Science (sfcs 1986) · doi:10.1109/sfcs.1986.25 · Cited by 2,281
Mining high-speed data streams
Domingos, P., & Hulten, G. (2000) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/347090.347107 · Cited by 2,217
Privacy-Preserving Public Auditing for Secure Cloud Storage
Wang, C., Chow, S. S. M., Wang, Q., Ren, K., & Lou, W. (2011) · IEEE Transactions on Computers · doi:10.1109/tc.2011.245 · Cited by 1,382
Map-Reduce for Machine Learning on Multicore
Chu, C.-T., Kim, S. K., Lin, Y.-A., Yu, Y., Bradski, G. R., Ng, A. Y., & Olukotun, K. (2007) · The MIT Press eBooks · doi:10.7551/mitpress/7503.003.0040 · Cited by 1,247
Dewdney, P. E., Hall, P. J., Schilizzi, R. T., & Lazio, T. J. W. (2009) · Proceedings of the IEEE · doi:10.1109/jproc.2009.2021005 · Cited by 1,196
Challenges and opportunities with big data
Labrinidis, A., & Jagadish, H. V. (2012) · Proceedings of the VLDB Endowment · doi:10.14778/2367502.2367572 · Cited by 1,070
Research works printed but not linked: 4
- gillick, 2006, Mapreduce Distributed Computing for Machine Learning
- ghoting, 2009, Hadoop-ML: An Infrastructure for the Rapid Implementation of Parallel Reusable Analytics, Proc Large-Scale Machine Learning Parallelism and Massive Data Sets Workshop (NIPS '09)
- bughin, 2010, Clouds Big Data and Smart Assets Ten Tech-enabled Business Trends to Watch
- reed, 2011, Real Time Machine Learning to Find Fast Transient Radio Anomalies: A Semi-Supervised Approach Combining Detection and RFI Excision, Proc Int'l Astronomical Union Symp Time Domain Astronomy
Cited by
Sorted by citations
A review of clustering techniques and developments
Saxena, A. K., Prasad, M., Gupta, A., Bharill, N., Patel, O. P., Tiwari, A., Er, M. J., Ding, W., & Lin, C. (2017) · Neurocomputing · doi:10.1016/j.neucom.2017.06.053 · Cited by 1,469
Self-citationEl-Kassar, A.-N., & Singh, S. K. (2018) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2017.12.016 · Cited by 1,414
A survey on deep learning for big data
Zhang, Q., Yang, L. T., Chen, Z., & Li, P. (2018) · Information Fusion · doi:10.1016/j.inffus.2017.10.006 · Cited by 1,226
Lei, Y., Jia, F., Lin, J., Xing, S., & Ding, S. X. (2016) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2016.2519325 · Cited by 1,191
A survey of heterogeneous information network analysis
Shi, C., Li, Y., Zhang, J., Sun, Y., & Yu, P. S. (2016) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2016.2598561 · Cited by 1,148
Metadata
- Authors
- Wu, X., Zhu, X., Wu, G.-Q., & Ding, W.
- Affiliations
- Hefei University of Technology (CN)
- University of Massachusetts Boston (US)
- ORCID
- Venue
- IEEE Transactions on Knowledge and Data Engineering
- Published
- 2014-01
- Volume
- 26
- Issue
- 1
- Pages
- 97-107
- ISSN
- 1041-4347 (ISSN-L)
- OpenAlex
- W2040263621
- Cited by
- 2,410
- References
- 53
- Citations by year
- 2026: 37 · 2025: 51 · 2024: 66 · 2023: 96 · 2022: 121 · 2021: 224 · 2020: 271 · 2019: 305 · 2018: 321 · 2017: 323 · 2016: 291 · 2015: 231 · 2014: 71
- FWCI
- 434.88
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Knowledge and Data Engineering (publishedVersion)
- Topics
- Social Sciences › Decision Sciences › Information Systems and Management › Big Data Technologies and Applications
- Physical Sciences › Computer Science › Information Systems › Information Retrieval and Data Mining
- Physical Sciences › Computer Science › Information Systems › Data Mining Algorithms and Applications
- Keywords
- big data, data mining, data-driven modeling, information sources, user interest modeling, data security and privacy
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