Please wait a minute...

当期目录

    15 May 2019, Volume 5 Issue 3
    TOPIC:BIG DATA GOVERNANCE
    Framework of government big data governance system and effective way of implementation
    Xiaomi AN, Mingjun GUO, Xuehai HONG, Wei WEI
    2019, 5(3):  3-12.  doi:10.11959/j.issn.2096-0271.2019019
    Asbtract ( 1183 )   HTML ( 243)   PDF (1135KB) ( 984 )   Knowledge map   
    Figures and Tables | References | Related Articles | Metrics

    Limited by single disciplinary perspective from disparate aspects at different levels,there is lack of systematic study on big data governance system building in current literature.A systematic review of literature and case studies under multidisciplinary and meta-synthetic thinking was conducted.Three levels of system framework consisting of the macro-level,meso-level and micro-level as an organic whole were proposed,forming the cooperation alliance of multi-subjects at the macro-level for effective collaboration and co-governance,the multi-level activity processes at the meso-level for effective communication and co-existence,and multi-dimensional elements at the micro-level for effective connectivity and winwin.Such a proposed framework has theoretical significance and pragmatic value to improve the interdependence,interconnectivity and interaction of the key components of a big data governance system.

    Data wrangling:a key technique of data governance
    Xiaoyong DU, Yueguo CHEN, Ju FAN, Wei LU
    2019, 5(3):  13-22.  doi:10.11959/j.issn.2096-0271.2019020
    Asbtract ( 3293 )   HTML ( 1160)   PDF (1421KB) ( 2200 )   Knowledge map   
    Figures and Tables | References | Related Articles | Metrics

    Data is an important resource for governments,businesses and institutions.Data governance focuses on many aspects of effective use of data resources,such as data asset,data management,data sharing,and data privacy.A key techniquedata wrangling-in data governance from the perspective of data management was explored.The key technologies of data wrangling based on data owners and direct users-industry users were emphasized,including data structure processing,data quality assessment and data cleaning,data normalization,data fusion and extraction,data publishing and sharing,etc.Finally,some thoughts on strengthening the research on data organization were put forward.

    Research on supervising big data governance method for securities and futures industry
    Dongxing JIANG, Ruonan GAO, Haoyu WANG
    2019, 5(3):  23-34.  doi:10.11959/j.issn.2096-0271.2019021
    Asbtract ( 525 )   HTML ( 46)   PDF (1477KB) ( 492 )   Knowledge map   
    Figures and Tables | References | Related Articles | Metrics

    In order to give full play to the asset value of data,the supervisory department of the China Securities Regulatory Commission attaches great importance to the management of big data.Through sorting out the demand and particularity of big data governance in securities and futures industry,a thorough study on the big data governance system of securities and futures industry was made,which mainly includes four aspects.Through the implementation of the data engineering construction of the China Securities Regulatory Commission,the deep-seated problems in the management of big data in the securities and futures industry were further discovered,such as co-construction and sharing of projects,multi-source data checking,and so on.Finally,a super large data management platform was proposed to guarantee the construction of big data engineering in an all-round way and provide more comprehensive,scientific and objective support for supervision and decision-making.

    “Intelligent Court” data fusion analysis and integrated application
    Yongbin QIN, Li FENG, Yanping CHEN, Ruizhang HUANG, Yulei LIU, Hongfa DING
    2019, 5(3):  35-46.  doi:10.11959/j.issn.2096-0271.2019022
    Asbtract ( 650 )   HTML ( 98)   PDF (2138KB) ( 811 )   Knowledge map   
    Figures and Tables | References | Related Articles | Metrics

    In view of the common problems and practical needs in the construction of “Intelligent Court”,the framework of “Intelligent Court” data fusion analysis and integration application demonstration platform was introduced.The research ideas and implementation paths of common key technologies in the construction of “Intelligent Court” were put forward from four aspects:deep semantic learning of judicial big data,judicial data fusion based on knowledge map,judicial data security protection and privacy protection,and visualization of judicial data fusion analysis.Finally,taking evidence extraction,criminal chain construction and legal provisions recommendation as examples,the application effect of data fusion analysis and integrated application demonstration platform was analyzed.The research results have certain reference value for realizing the goal of building a new generation of “Intelligent Court” with the judicial data of courts as the core.

    Study on big data governance standard system
    Hong DAI, Qun ZHANG, Zhuo YIN
    2019, 5(3):  47-54.  doi:10.11959/j.issn.2096-0271.2019023
    Asbtract ( 1446 )   HTML ( 232)   PDF (1286KB) ( 1239 )   Knowledge map   
    Figures and Tables | References | Related Articles | Metrics

    As big data has gradually stepped from the concept introduction period to the new stage of deepening pragmatic application,big data governance has become a new hot spot in the big data industry ecosystem.Its development requires the foundation of standard system construction and the support of standardization.The problems faced by China’s big data governance standardization were sorted out,the concepts and definitions related to big data governance were clarified,the big data governance standard system framework was proposed,and suggestions for the future standardization work were given,which can help the industry to build a new big data standard system which covering big data governance,and provide standardized support for China’s new stage of the development of big data technology and industrial.

    STUDY
    Development prospect of integrated transportation big data application technology
    Xiaobo LIU, Yangsheng JIANG, Youhua TANG, Yibin ZHANG, Zilan WANG, Jie LUO
    2019, 5(3):  55-68.  doi:10.11959/j.issn.2096-0271.2019024
    Asbtract ( 773 )   HTML ( 132)   PDF (1354KB) ( 719 )   Knowledge map   
    References | Related Articles | Metrics

    The continuous development of advanced technologies such as big data,artificial intelligence,cloud computing,Internet of things and intelligent terminals provide useful big data resources and technical support for the integrated,intelligent and wise development of integrated transportation.Aiming at the cruxes of integrated traffic planning and design,operation management and control,safety and environmental protection maintenance,and logistics service,such as poor quality of heterogeneous data,isolated information island,low degree of mining,etc.,the key technical points needed to be broken through in the development of basic technology and application technology of big data for integrated transportation were prospected,and some references for a better and more systematic application of big data in integrated transportation were provided.

    Edge intelligence:state-of-the-art and expectations
    Kenli LI, Chubo LIU
    2019, 5(3):  69-75.  doi:10.11959/j.issn.2096-0271.2019025
    Asbtract ( 1426 )   HTML ( 268)   PDF (1110KB) ( 1312 )   Knowledge map   
    Figures and Tables | References | Related Articles | Metrics

    Edge intelligence (EI,which merges artificial intelligence (AI) into edge computing and deploys AI methods on edge devices) is regarded as a very efficient measure to provide faster and better intelligent services,having been successfully applied to various fields.However,current EI faces great difficulties.Firstly,a brief introduction to EI was given,and then,three challenges in EI were summarized.Finally,current five research directions for solving the EI challenges were outlined.The paper was expected to provide a better understanding for people who want to know EI,and help for researchers who study EI to have an overall direction guideline.

    Research on the diffusion models and transfer characteristic of local big data policy in China
    Wenyao DING, Zili ZHANG, Guoxian YU, Yi HAN
    2019, 5(3):  76-95.  doi:10.11959/j.issn.2096-0271.2019026
    Asbtract ( 394 )   HTML ( 27)   PDF (1772KB) ( 365 )   Knowledge map   
    Figures and Tables | References | Related Articles | Metrics

    To reveal the development trend and content relevance of local big data policy in China,based on the theory of policy diffusion and policy transfer,the models of time-space diffusion and the characteristics of content transfer for 68 local big data policies in various provinces and cities in China were explored by using the methods of content analysis and social network analysis.The study finds that the time-spreading model of China local big data policy release and reference is in line with the general law of policy diffusion,and the time-diffusion curve is S-type.The policy reference diffusion models are “central-local diffusion” and “top-down diffusion”,which are common in China.In addition,local big data policies have a high inheritance of important national policy content,and policy innovations vary from region to region.

    Study on direction and strength of relation based on knowledge graph
    Genlin ZANG, Yaqiang WANG, Qingrong WU, Chunli ZHAN, Xinyang XIE
    2019, 5(3):  96-103.  doi:10.11959/j.issn.2096-0271.2019027
    Asbtract ( 505 )   HTML ( 59)   PDF (1594KB) ( 498 )   Knowledge map   
    Figures and Tables | References | Related Articles | Metrics

    In current popular ideas for knowledge graph construction,the relations in graphs were described by words,it is difficult to calculate the relations in graphs.To this issue,concepts of the direction,intensive factors,temporal factors of relations were proposed.Automatic models of positive,negative,intensive and temporal relations can be formed through supervised machine learning,so that the quantitative calculation of the relations can be implemented in the domain knowledge graph.This method forms a new idea in many areas such as calculating the trend of incidents,calculating the change of cooperation and competition between enterprises,and analyzing the market expansion of sales people.It is meaningful for artificial intelligence to be applied in specific industries.

Most Download
Most Read
Most Cited