[1]杨明,李铁冰,姜茸,等.基于AHP 的大数据可用性及挖掘方案模型研究[J].计算机技术与发展,2018,28(05):51-54.[doi:10.3969/j.issn.1673－629X.2018.05.012]
　YANG Ming,LI Tie-bing,JIANG Rong,et al.Research on Model of Big Data Usability and Mining Strategy Based on AHP[J].,2018,28(05):51-54.[doi:10.3969/j.issn.1673－629X.2018.05.012]
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基于AHP 的大数据可用性及挖掘方案模型研究()

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
28
期数:
2018年05期
页码:
51-54
栏目:
智能、算法、系统工程
出版日期:
2018-05-10

文章信息/Info

Title:
Research on Model of Big Data Usability and Mining Strategy Based on AHP
文章编号:
1673-629X(2018)05-0051-04
作者:
杨明; 李铁冰; 姜茸; 高提雷; 王佳
云南财经大学 信息学院，云南 昆明 650221
Author(s):
YANG Ming; LI Tie-bing; JIANG Rong; GAO Ti-lei; WANG Jia
School of Information,Yunnan University of Finance and Economics,Kunming 650221,China
关键词:
大数据; 数据可用性; 层次分析法; 大数据挖掘; 可用性评价
Keywords:
big data; data usability; AHP; big data mining; usability evaluation
分类号:
TP31
DOI:
10.3969/j.issn.1673－629X.2018.05.012
文献标志码:
A
摘要:
大数据本身规模庞大、类型复杂的特点，使其价值在庞大数据的包裹下充满了不确定性。面对大数据，如若无法对其可用性进行分析，并加以有效的数据处理方法，其应用价值也就无法体现。目前，对于大数据的可用性分析虽然已经提出了不少方法，但是对其可用性的评估方案却较少，尤其是定量的研究分析。对此，需要梳理大数据的可用性影响因素，并结合数学方法，建立大数据可用性及挖掘方案的研究模型。在该模型的基础上，以提高数据的可用性为目标，围绕影响大数据可用性的因素，针对不同的数据挖掘方案进行了定量的比较分析，探讨提高大数据可用性的可行方案。
Abstract:
The big data itself is large and complex,and its value is full of uncertainty under the package of huge data.In the face of big data，its application value cannot be reflected if its usability cannot be analyzed and the effective data processing method cannot be applied.At present，many methods have been proposed for the usability analysis of big data,but there are few for the usability evaluation,especially the quantitative analysis.For this,it needs to sort out the factors affecting the usability of big data,and combined with mathematical methods a research model about big data usability and mining strategy is established．On the basis,in order to improve the availability of data,the feasible scheme of improving big data availability is discussed based on the factors which affect the big data availability for different data mining scheme to carry on the quantitative comparative analysis.

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更新日期/Last Update: 2018-06-28