期刊文献+

电网企业大数据在财务决策中的应用研究 被引量:5

Research on Application of Big Data on Financial Decision-making of Power Grid Enterprises
下载PDF
导出
摘要 大数据是一个世界范围内、各行各业都日益重视的话题,然而,大数据代表什么、电力企业如何应用大数据,这些问题的理解者或许并没有那么多,本文的目的有两个:一是通过阅读本文,读者能够对大数据有个全面的正确的认识和理解;二是以某电网企业为例,运用各种数据分析工具(包括时间序列分析、回归分析、聚类分析),将大数据理念与实际的电网企业财务决策挂钩,通过分析两个财务决策情景来展示大数据的财务应用,让对大数据仍有模糊认识的读者更加清晰、形象地看到大数据是如何应用的,以此来加快我国大数据的应用发展。通过案例应用可以看到大数据的确可以为电网企业发挥重要作用。 Big data is an increasingly important topic in the world,however,what big data meaning and how to apply is still unclear.There are two aims in this article:firstly,it give the readers a comprehensive understanding about big data;secondly,takes one grid enterprise for example to show how to use big data through all kinds of analytic tools(including time series analysis、cluster analysis、regression analysis),two scenarios about financial decision-making are demonstrated in order to display the application of big data.The results show that big data will and can make a difference in grid enterprise.
作者 杨进
出处 《华北电力大学学报(社会科学版)》 2016年第2期33-39,共7页 Journal of North China Electric Power University(Social Sciences)
关键词 大数据 智能电网 财务 时间序列分析 聚类分析 big data smart power grids finance time series analysis cluster analysis
  • 相关文献

参考文献7

二级参考文献31

  • 1ZHU BIN, WANG ANBAO. The storage technology for GIS data re- alization [J]. Journal of Computers, 2011, 10(6): 2229-2236. 被引量:1
  • 2DOAN A, NAUGHTON J F, BAID A, et al. The case for a struc- tured approach to managing unstructured data [ EB/OL]. [ 2011 - 10- 11 ]. https://database, cs. wisc. edu/cidr/cidr2009/Paper_ 110. pdf. 被引量:1
  • 3ZHANG XIAO, DU XIAO-YONG, CHEN JIN-CHUAN, et al. Managing a large shared bank of unstructured data by using free-ta- ble {G]// APWEB'IO: Proceedings of the 2010 12th International Asia-Pacific Web Conference. Washington, DC: IEEE Computer Society, 2010:441-446. 被引量:1
  • 4VILACA R, OLIVEIRA R. Clouder: a flexible large scale decen- tralized object store: architecture overview [ C]// WDDDM 2009: Proceedings of the Third Workshop on Dependable Distributed Data Management. New York: ACM, 2009:25-28. 被引量:1
  • 5VAHDAT A, AL-FARES M, FARRINGTON N, et al. Scale-out net- working in the data center [J]. IEEE Micro, 2010, 30(4): 29 -41. 被引量:1
  • 6LIN YUNFENG, LIANG BEN, LI BAOCHUN. Priority random lin- ear codes in distributed storage systems [ J]. 1EEE Transactions on Parallel and Distributed Systems, 2009, 20(11): 1653 -1667. 被引量:1
  • 7DORNBACH J, ROEDEL M, KEHR J, et al. Enterprise service o- riented architecture for large file handling with document manage- ment system: US, 7899922[ P]. 2011 -03 - 01. 被引量:1
  • 8ANGSKUN T, FAGG G, BOSILCA G, et al. Self-healing network for scalable fault-tolerant runtime environments [ J]. Future Genera- tion Computer Systems, 2010, 26(3): 479-485. 被引量:1
  • 9GOTTUMUKKALA N R, NASSAR R, PAUN M, et al. Reliability of a system of k nodes for high performance computing applications [ J]. IEEE Transactions on Reliability, 2010, 59(1) : 162 -169. 被引量:1
  • 10CALDERON A, GARCfA-CARBALLEIRA F, SANCHEZ L M, et al. Fault tolerant file models for parallel file systems: Introducing distribution patterns for every file [ J]. The Journal of Supercomput- ing, 2009, 47(3): 312-334. 被引量:1

共引文献183

同被引文献44

引证文献5

二级引证文献35

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部