从大数据到大知识:HACE+BigKE

吴信东; 何进; 陆汝钤; 郑南宁

doi:10.16383/j.aas.2016.c160239

从大数据到大知识:HACE+BigKE

doi: 10.16383/j.aas.2016.c160239

吴信东^1,2, ,,
何进^1,,
陆汝钤^3,,
郑南宁^4,

1.
合肥工业大学计算机与信息学院合肥 230009 中国
2.
佛蒙特大学计算机科学系伯灵顿 VT05405 美国
3.
中国科学院数学与系统科学研究院北京 100190 中国
4.
西安交通大学人工智能与机器人研究所西安 710049 中国

基金项目:

教育部长江学者和创新团队发展计划“多源海量动态信息处理” IRT13059

国家重点基础研究发展计划（973计划） 2013CB329604

国家自然科学基金 61229301

详细信息

作者简介:
何进合肥工业大学计算机与信息学院硕士研究生.2015年获得安徽财经大学计算机科学与技术系学士学位.主要研究方向为数据挖掘和大数据分析.E-mail:flyingfish93319@126.com

陆汝钤中国科学院院士.1959年获得德国耶拿大学数学系学士学位.主要研究方向为知识工程, 基于知识的软件工程, 人工智能.E-mail:rqlu@math.ac.cn

郑南宁：ZHENG Nan-Ning Member of the Chinese Academy of Engineering, IEEE Fellow, and professor at Xi'an Jiaotong University. He received his Ph. D. degree from Keio University (Japan) in 1985. His research interest covers pattern recognition, machine vision, and image processing

通讯作者:
吴信东长江学者, IEEE Fellow, AAAS Fellow.合肥工业大学计算机与信息学院教授.美国佛蒙特大学计算机与科学系教授.1993年获得英国爱丁堡大学人工智能博士学位.主要研究方向为数据挖掘, 知识库系统, 万维网信息探索.本文通信作者.E-mail:xwu@hfut.edu.cn

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出版历程
- 收稿日期: 2016-03-03
- 录用日期: 2016-05-31
- 刊出日期: 2016-07-01

From Big Data to Big Knowledge: HACE+BigKE

WU Xin-Dong^{1,2
, ,},
HE Jin^1
,,
LU Ru-Qian^3
,,
ZHENG Nan-Ning^4
,

1.
School of Computer Science and Information Engineering, Hefei University of Technology, Hefei 230009, China
2.
Department of Computer Science, University of Vermont, Burlington VT 05405, USA
3.
Institute of Mathematics, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China
4.
Institute of the Artificial Intelligence and Robotics, Xi'an Jiaotong University, Xi'an 710049, China

Funds:

Program for Changjiang Scholars and Innovative Research Team in University (PCSIRT) of the Ministry of Education of China IRT13059

Supported by National Basic Research Program of China (973 Program) 2013CB329604

National Natural Science Foundation of China 61229301

More Information

Author Bio:
Master student at the College of Computer Science and Information Engineering, Hefei University of Technology. She received her bachelor degree from Anhui Finance and Economics University in 2015. Her research interest covers data mining and big data analytics

Member of the Chinese Academy of Sciences. He received his bachelor degree from the University of Jena (Germany) in 1959. His research interest covers knowledge engineering, knowledge based software engineering, and artificial intelligence

Corresponding author: WU Xin-Dong Professor at the College of Computer Science and Information Engineering, Hefei University of Technology; professor in the Department of Computer Science, the University of Vermont. He received his Ph. D. degree from the University of Edinburgh in 1993. His research interest covers data mining, knowledge based systems, and Web information exploration. Corresponding author of this paper

摘要

摘要: 大数据面向异构自治的多源海量数据，旨在挖掘数据间复杂且演化的关联.随着数据采集存储和互联网技术的发展，大数据分析和应用已成为各行各业的研发热点.本文从大数据的本质特征开始，评述现有的几种大数据模型，包括5V，5R，4P和HACE定理，同时从知识建模的角度，介绍一种大数据知识工程模型BigKE来生成大知识，并对大知识的前景进行展望.
- 大数据 /
- 知识挖掘 /
- 异构 /
- 碎片化知识 /
- 在线学习
Abstract: Big data deals with heterogeneous and autonomous multi-sources, and aims at mining complex and evolving relationships among data. With the fast development of data collection, data storage and networking technologies, big data analytics has become a hot topic for research and development in various fields. This paper starts with the essential characteristics of big data, reviews existing popular models for big data, including 5V, 5R, 4P and the HACE theorem. Also, from the viewpoint of knowledge modeling, this paper introduces BigKE, a big data knowledge engineering model for big knowldedge, and discusses the challenges and opportunities of big knowledge research and development.
- Big data /
- knowledge mining /
- heterogeneity /
- fragmented knowledge /
- online learning

HTML全文

图 1 大数据处理框架的修改版[15]

Fig. 1 A big data processing framework updated form[15]

下载: 全尺寸图片幻灯片

图 2 大数据知识工程模型——BigKE ^[39]

Fig. 2 quad Big data knowledge engineering——BigKE ^[39]

下载: 全尺寸图片幻灯片

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