大数据与化学数据挖掘.doc

大数据与化学数据挖掘.doc大数据与化学数据挖掘
刘言蔡文生邵学广
南开大学化学学院分析科学研究中心
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摘    要:
数据是重要的战略资源,大数据挖掘技术已成为学术界、,综述了与化学研究有关的大数据研究状况,讨论了大数据在基础理论与关键技术2个层面上的主要问题以及大数据挖掘技术在化学各领域中的应用,并对大数据发展的未来及其在化学学科中的应用前景进行了展望.
关键词:
大数据; 数据挖掘; 可视化; 云计算; 化学;
作者简介:邵学广,E-mail: ******@nankai.
收稿日期:2014-11-03
Big data and chemical data mining
LIU Yan CAI WenSheng SHAO XueGuang
Research Center for Analytical Sciences, College of Chemistry, Nankai University;
Abstract:
Big data is fast ing an important resource and a hot topic in academic research, business and government. In this paper, we introduce the concept of big data, and review advances in big data research, including technology for big data collection, puting technology like Google's file system, Big Table, Map Reduce and Hadoop, and data mining and visualization methods for big data. Big data monly defined by the so-called 4 V's, ., volume, variety, velocity, and value. High volume data with large variety make the analysis of big data much more difficult. Since velocity is important, fast high performance analysis methods are needed for big data. Moreover, the high value of big data is precisely the reason for the importance of and research activity in this area. In this paper, we also summarize various applications of big data in chemistry. Professional information platforms like the Collaboratory for Multi-scale Chemical Sciences(CMCS) and Chemical Informatics and Cyberinfrastructure Collaboratory() have been developed to manage and research chemical big data, while search engines like the Chem DB Portal have been established to extract chemical information from the . Software like the Integrated Project View and ArQ iologist can be used to assist in the design of new medicines in medicinal chemistry. A data management system called BioG ames has been proposed to analyze microfluidics big data. Moreover, graphics processing units are widely us