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
Identifying influential and susceptible members of social networks
Aral, S., & Walker, D. (2012) · Science · doi:10.1126/science.1215842 · Cited by 1,047
Evaluating MapReduce for Multi-core and Multiprocessor Systems
Ranger, C., Raghuraman, R., Penmetsa, A., Bradski, G. R., & Kozyrakis, C. (2007) · IEEE International Symposium on High-Performance Computer Architecture (HPCA) · doi:10.1109/hpca.2007.346181 · Cited by 968
SPRINT: A Scalable Parallel Classifier for Data Mining
Shafer, J. C., Agrawal, R., & Mehta, M. (1996) · Cited by 787
Howe, D. G., Costanzo, M. C., Fey, P., Gojobori, T., Hannick, L. I., Hide, W., Hill, D. P., Kania, R., Schaeffer, M. L., Pierre, S. S., et al. (2008) · Nature · doi:10.1038/455047a · Cited by 701
Jacobs, A. L. (2009) · Communications of the ACM · doi:10.1145/1536616.1536632 · Cited by 680
Privacy Preserving Data Mining
Lindell, Y., & Pinkas (2002) · Journal of Cryptology · doi:10.1007/s00145-001-0019-2 · Cited by 675
Missing Data, Imputation, and the Bootstrap
Efron, B. (1994) · Journal of the American Statistical Association · doi:10.1080/01621459.1994.10476768 · Cited by 471
Online Feature Selection with Streaming Features
Wu, X., Yu, K., Ding, W., Wang, H., & Xingquan Zhu, A. (2012) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2012.197 · Cited by 285
Self-citationPapadimitriou, S., & Sun, J. (2008) · Proceedings · doi:10.1109/icdm.2008.142 · Cited by 207
Synthesizing high-frequency rules from different data sources
Wu, X., & Zhang, S. (2003) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2003.1185839 · Cited by 169
Self-citationDas, S., Sismanis, Y., Beyer, K. S., Gemulla, R., Haas, P. J., & McPherson, J. (2010) · ACM SIGMOD International Conference on Management of Data (SIGMOD) · doi:10.1145/1807167.1807275 · Cited by 164
The changing privacy landscape in the era of big data
Schadt, E. E. (2012) · Molecular Systems Biology · doi:10.1038/msb.2012.47 · Cited by 161
(2008) · Nature · doi:10.1038/455001a · Cited by 142
Active Learning From Stream Data Using Optimal Weight Classifier Ensemble
Xingquan Zhu, A., Zhang, P., Lin, X. S., & Shi, Y. (2010) · IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) · doi:10.1109/tsmcb.2010.2042445 · Cited by 138
Database classification for multi-database mining
Wu, X., Zhang, C., & Zhang, S. (2003) · Information Systems · doi:10.1016/j.is.2003.10.001 · Cited by 130
Self-citationMining With Noise Knowledge: Error-Aware Data Mining
Wu, X., & Zhu, X. (2008) · IEEE Transactions on Systems Man and Cybernetics - Part A Systems and Humans · doi:10.1109/tsmca.2008.923034 · Cited by 109
Self-citationBirney, E. (2012) · Nature · doi:10.1038/489049a · Cited by 107
Agencies Rally to Tackle Big Data
Mervis, J. D. (2012) · Science · doi:10.1126/science.336.6077.22 · Cited by 95
Mitchell, T. M. (2009) · Science · doi:10.1126/science.1174459 · Cited by 92
Information propagation in online social networks: a tie-strength perspective
Zhao, J., Wu, J., Xu, F., Xiong, H., & Xu, K. (2011) · Knowledge and Information Systems · doi:10.1007/s10115-011-0445-x · Cited by 89
Collective Mining of Bayesian Networks from Distributed Heterogeneous Data
Chen, R., Sivakumar, K., & Kargupta, H. (2004) · Knowledge and Information Systems · doi:10.1007/s10115-003-0107-8 · Cited by 86
The Role of Domain Knowledge in a Large Scale Data Mining Project
Kopanas, I., Avouris, N. M., & Daskalaki, S. (2002) · Lecture notes in computer science · doi:10.1007/3-540-46014-4_26 · Cited by 75
Shroud: ensuring private access to large-scale data in the data center
Lorch, J. R., Parno, B. J., Mickens, J. W., Raykova, M., & Schiffman, J. (2013) · File and Storage Technologies · doi:10.5555/2591272.2591294 · Cited by 72
Efficient algorithms for influence maximization in social networks
Chen, Y.-C., Peng, W.-C., & Lee, S.-Y. (2012) · Knowledge and Information Systems · doi:10.1007/s10115-012-0540-7 · Cited by 68
Big data deserve a bigger audience
Huberman, B. A. (2012) · Nature · doi:10.1038/482308d · Cited by 67
Cormode, G., & Srivastava, D. (2009) · ACM SIGMOD International Conference on Management of Data (SIGMOD) · doi:10.1145/1559845.1559968 · Cited by 57
Machanavajjhala (2012) · doi:10.1145/2331042.2331051 · Cited by 56
Analyzing collective behavior from blogs using swarm intelligence
Banerjee, S., & Agarwal, N. (2012) · Knowledge and Information Systems · doi:10.1007/s10115-012-0512-y · Cited by 55
Algorithms for mining the evolution of conserved relational states in dynamic networks
Ahmed, R., & Karypis, G. (2012) · Knowledge and Information Systems · doi:10.1007/s10115-012-0537-2 · Cited by 49
Toolkit-Based High-Performance Data Mining of Large Data on MapReduce Clusters
Wegener, D., Möck, M., Adranale, D., & Wrobel, S. (2009) · IEEE ... International Conference on Data Mining workshops · doi:10.1109/icdmw.2009.34 · Cited by 45
Parallel algorithms for mining large-scale rich-media data
Chang, E. Y., Bai, H., & Zhu, K. (2009) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/1631272.1631451 · Cited by 41
Anonymizing classification data using rough set theory
Ye, M., Wu, X., Hu, X., & Hu, D. (2013) · Knowledge-Based Systems · doi:10.1016/j.knosys.2013.01.007 · Cited by 37
Self-citationDuncan, G. T. (2007) · Science · doi:10.1126/science.1143464 · Cited by 33
Online active multi-field learning for efficient email spam filtering
Liu, W., & Wang, T. (2011) · Knowledge and Information Systems · doi:10.1007/s10115-011-0461-x · Cited by 33
A logical framework for identifying quality knowledge from different data sources
Su, K., Huang, H., Wu, X., & Zhang, S. (2006) · Decision Support Systems · doi:10.1016/j.dss.2006.02.012 · Cited by 32
Self-citationA clustering approach for sampling data streams in sensor networks
Silva, A. D., Chiky, R., & Hébrail, G. (2011) · Knowledge and Information Systems · doi:10.1007/s10115-011-0448-7 · Cited by 32
Novel approaches to crawling important pages early
Alam, M. H., Ha, J., & Lee, S. (2012) · Knowledge and Information Systems · doi:10.1007/s10115-012-0535-4 · Cited by 25
Parallelization with Multiplicative Algorithms for Big Data Mining
Luo, D., Ding, C. H. Q., & Huang, H. (2012) · Proceedings · doi:10.1109/icdm.2012.155 · Cited by 24
Building Intelligent Learning Database Systems
Wu, X. (1997) · doi:10.1609/aimag.v21i3.1524 · Cited by 9
Self-citationLazio, J. (2009) · University of Groningen research database (University of Groningen / Centre for Information Technology) · Cited by 6
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
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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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