This Article 
   
 Share 
   
 Bibliographic References 
   
 Add to: 
 
Digg
Furl
Spurl
Blink
Simpy
Google
Del.icio.us
Y!MyWeb
 
 Search 
   
Data mining with big data
Jan. 2014 (vol. 26 no. 1)
pp. 97-107
Xindong Wu, Sch. of Comput. Sci. & Inf. Eng., Hefei Univ. of Technol., Hefei, China
Xingquan Zhu, Dept. of Comput. & Electr. Eng. & Comput. Sci., Florida Atlantic Univ., Boca Raton, FL, USA
Gong-Qing Wu, Sch. of Comput. Sci. & Inf. Eng., Hefei Univ. of Technol., Hefei, China
Wei Ding, Comput. Sci. Dept., Univ. of Massachusetts Boston, Boston, MA, USA
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.
Index Terms:
Information management,Data handling,Data storage systems,Data privacy,Data models,Distributed databases,complex and evolving associations,Big Data,data mining,heterogeneity,autonomous sources
Citation:
Xindong Wu, Xingquan Zhu, Gong-Qing Wu, Wei Ding, "Data mining with big data," IEEE Transactions on Knowledge and Data Engineering, vol. 26, no. 1, pp. 97-107, Jan. 2014, doi:10.1109/TKDE.2013.109
Usage of this product signifies your acceptance of the Terms of Use.