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| Quantitative Evaluation of Big Data Policies from the Perspective of Information Process:Taking 11 National Big Data Policies as an Example |
| Hu Feng1,2, Wen Zhiqiang3, Shen Jinqiu1,2, Yao Yuan4 |
1.Jiangsu Academy of Science and Technology for Development,Nanjing 210042,China;
2.Jiangsu Information Institute of Science and Technology,Nanjing 210042,China;
3.College of Politics and Public Administration,Tianjin Normal University,Tianjin 300387,China;
4.College of Cultural Industries,Nanjing University of the Arts,Nanjing 210013,China |
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Abstract This paper took 11 big data policies issued by central government departments from 2015 to 2019 as samples,and took Herring model as the research framework.It evaluated the big data policies quantitatively from the perspective of the process of identification,planning,collection,processing,analysis and demonstration of information needs.Based on the PMC index model,the PMC index value and depression degree of 11 big data policies were calculated,and the merits and demerits of each big data policy were evaluated concretely,and the referential policy improvement path were put forward.The result shows that the overall design of big data policies is scientific and reasonable,and the quality of policies is proven to be high.Among these policies,two policies' level are perfect,seven are excellent,and two are acceptable.However,the scores on the four factors of policy timeliness,policy tools,policy audience and policy content are low,which requires improvement in the future.
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Received: 11 July 2019
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[1]李辉.大数据推动我国经济高质量发展的理论机理、实践基础与政策选择[J].经济学家,2019 (3):52-59.
[2]中国信息通信研究院.大数据白皮书 (2018年)[R].北京:中国信息通信研究院,2018.
[3]范梓腾,谭海波.地方政府大数据发展政策的文献量化研究——基于政策 “目标—工具”匹配的视角[J].中国行政管理,2017 (12):46-53.
[4]孙鸿飞,张海涛.基于文献计量与可视化方法的国内外大数据领域研究动态研究[J].情报科学,2018,36 (11):169-176.
[5]阿尔文·托夫勒.第三次浪潮[M].北京:中信出版社,2006.
[6]赵蓉英,魏明坤.基于可视化图谱的国内外大数据研究[J].情报科学,2016,34 (12):3-16.
[7]RAGHUPATHI W,RAGHUPATHI V.Big data analytics in healthcare:promise and potential[J].Health information science and systems,2014,2 (1):3.
[8]SOMASEKHAR G,KARTHIKEYAN K.The novel big data algorithm for distributional instance learning[J].Ain shams engineering journal,2018,9 (4):2631-2640.
[9]PARK J K,KIM J.Big data storage configuration and performance evaluation utilizing NDAS storage systems[J].AKCE international journal of graphs and combinatorics,2018,15 (2):197-201.
[10]SAHOO P K,MOHAPATRA S K,WU S L.SLA based healthcare big data analysis and computing in cloud network[J].Journal of parallel and distributed computing,2018 (119):121-135.
[11]OSMAN A M S.A novel big data analytics framework for smart cities[J].Future generation computer systems,2019 (91):620-633.
[12]FUMEO E,ONETO L,ANGUITA D.Condition based maintenance in railway transportation systems based on big data streaming analysis[J].Procedia computer science,2015 (53):437-446.
[13]KWON T H,KWAK J H,KIM K.A study on the establishment of policies for the activation of a big data industry and prioritization of policies:lessons from Korea[J].Technological forecasting and social change,2015 (96):144-152.
[14]彭知辉.论政府在大数据发展中的作用:以大数据政策为视角[J].广东行政学院学报,2017,29 (1):12-20.
[15]李月,侯卫真,李琳琳.我国地方政府大数据战略研究[J].情报理论与实践,2017,40 (10):31-35,46.
[16]李樵.我国促进大数据发展政策工具选择体系结构及其优化策略研究[J].图书情报工作,2018,62 (11):5-15.
[17]周京艳,张惠娜,黄裕荣,等.政策工具视角下我国大数据政策的文本量化分析[J].情报探索,2016 (12):7-10.
[18]HERRING J P.Building a business intelligence system[J].Journal of business strategy,1988,9 (3):4-9.
[19]刘细文,刘锟发.企业竞争情报过程模型的关键机制分析[J].图书情报工作,2009,53 (24):8-11,73.
[20]ESTRADA M A R.Policy modeling:definition,classification and evaluation[J].Journal of policy modeling,2011 (33):523-536.
[21]张永安,郄海拓. “大众创业、万众创新”政策量化评价研究——以2017的10项双创政策情报为例[J].情报杂志,2018,37 (3):158-164,186.
[22]张永安,郄海拓.国务院创新政策量化评价——基于PMC指数模型[J].科技进步与对策,2017,34 (17):127-136.
[23]张永安,耿喆.我国区域科技创新政策的量化评价——基于PMC指数模型[J].科技管理研究,2015 (14):26-31.
[24]方永恒,陈友倩.国务院保障性住房政策量化评价——基于10项保障性住房政策情报的分析[J].情报杂志,2019,38 (3):101-107. |
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2020, Vol. 1 
