Data Mining with Big Data

Item

Title

Data Mining with Big Data

Abstract/Description

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.

Author/creator

Wu, Xindong

Zhu, Xingquan

Wu, Gong-Qing

Ding, Wei

Date

2014

In publication

IEEE Transactions on Knowledge and Data Engineering

Volume

26

Issue

1

Pages

97-107

Resource type

en Background/Context

Medium

en Print

Background/context type

en Conceptual

Keywords

heterogeneity

data mining

Big Data

autonomous sources

complex and evolving associations

Open access/free-text available

en Yes

Peer reviewed

en Yes

ISSN

1558-2191

DOI

10.1109/TKDE.2013.109

URL

Official Publisher's Webpage (IEEE Xplore)

Full-text PDF Shared by Author (Florida Atlantic University)

Citation

Wu, X., Zhu, X., Wu, G.-Q., & Ding, W. (2014). Data Mining with Big Data. IEEE Transactions on Knowledge and Data Engineering, 26(1), 97–107. https://doi.org/10.1109/TKDE.2013.109

Item sets

Handbook Chapter 19 Citations

• Email