摘要
用户需求漂移和服务供给失衡矛盾是制约大数据联盟发展核心问题之一。在当前服务经济发展新常态的大背景下,联盟服务供需能否有效匹配对于提高联盟运行效率、获取竞争优势等具有重要现实意义。本文利用模糊粗糙集、SVM、可拓理论进一步深入探究联盟服务供需匹配问题:一是在描述用户时序需求基础上,追踪和刻画用户需求漂移网络,二是提出一种基于模糊粗糙集和SVM集成的方法,实现对漂移网络中用户核心需求的识别,三是通过引入可拓理论构建大数据联盟的可拓服务模型。结果表明:针对大数据服务过程中用户需求随着时间进程发生漂移的现象,将基于模糊粗糙集的SVM需求识别器和可拓服务建模的方式相结合,能够更好地契合用户的动态需求,有助于联盟为需求漂移的用户提供有针对性的服务方案。
With the convergence and integration of cloud computing,mobile communication,intelligent terminal technology and internet economy,the global data presents the trend of rapid growth and massive aggregation,and the world enters the era of"data is king".The focus of a new round of international power competition has gradually shifted from the military battlefield to the control of big data resource.In order to promote the process of data resource innovation development in China,big data alliance comes into being.By recruiting the data companies and innovation teams in the industrial chain,it can excavate the hidden rules from the huge data set,provide decision support for the healthy development of the government,enterprises and social organizations,and gradually become the important carrier of implementation big data strategic actions.At present,the big data service consumption market has the good momentum of development,and presents the characteristics of refinement,comprehensiveness and scenario.However,the contradiction caused by user demand drift and big data service supply imbalance has become the bottleneck of orderly operation and development,which seriously affects the collaborative innovation efficiency of big data alliance.Therefore,how to provide targeted service solution for user with demand drift has become the key issue,which matters to the sustainable and healthy development of big data alliance in the new round of industrial competition.Most of these current studies are mainly theoretical exploration,and still in the research stage of qualitative analysis.There is no unified system theory to identify and measure the dynamic demand of user,especially the core demand identification in drift network is rarely mentioned.In addition,the research works about multi-agent collaborative service supply lack the integration of users′dynamic demand drift and flexible big data service supply into the unified research framework,especially in the context of big data alliance.Based on tracking and describing the dynamic track of user′s demand,this paper proposes the method of integrating fuzzy rough set and SVM,which is used to identify the core demand of user in the demand drift network.The main conclusions are as follows:(1)On the basis of tracking and describing the dynamic track of user′s demand,this paper reveals the phenomenon that user′s demand drifts with the process of time.Grasping the change of user′s demand timely is crucial for the effective provision of follow-up big data service.(2)A method based on integrating fuzzy rough set and SVM is proposed to identify the core demand of user in drift network.This is the premise and foundation for the alliance to provide service solution for user,and the extent to which meet the core demand of user is also the basis for the alliance to evaluate the service value.(3)On the basis of identifying the user′s core demand,the service module can be extended by using extension thinking,so as to improve the service pattern,function and content,providing targeted service solution for user.It is proved that the combination of SVM demand identifier based on fuzzy rough set and extension service modeling can better meet the dynamic demand of user.This paper does not consider the multi-tenant problem,billing strategy,service capability influencing factors in the actual service process of big data,which will be the main research direction in the future.
作者
高长元
胡艳玲
何晓燕
Gao Changyuan;Hu Yanling;He Xiaoyan(School of Economics and Management,Harbin University of Science and Technology,Harbin 150040,Heilongjiang,China)
出处
《科研管理》
CSSCI
CSCD
北大核心
2020年第7期221-229,共9页
Science Research Management
基金
国家自然科学基金面上项目:“大数据联盟云服务模式研究”(71672050,2017.01-2020.12)、“跨界联盟协同创新资源整合机制研究”(71774044,2018.01-2021.12)、“基于“云环境”的IT产业联盟知识转移与共享机制研究”(71272191,2013.01-2016.12)
黑龙江省哲学社会科学研究规划项目:“黑龙江省大数据产业联盟云服务模式研究”(16GLB01,2017.01-2020.12)
黑龙江省博士后基金项目:“黑龙江省移动云计算联盟数据价值挖掘研究”(LBH-Z15046,2015.12-2017.12)。
