大数据环境下基于公共服务平台的资源多级智能寻租与匹配策略和价值创造
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摘要
资源的高效寻租与匹配是其价值创造的关键。文中研究大数据环境下基于公共服务平台的资源寻租与匹配问题,针对公共服务资源的非结构化特点,考虑本体树的路径距离、连接深度和广度,重新定义了语义距离,提出了基于语义距离的五元组形式化描述模型,消除了公共服务资源在底层结构和类型上的复杂性;针对公共服务平台上资源及其相关数据信息规模巨大的问题,提出了资源多级智能寻租与匹配策略,首先通过对参数相对较少且简单的S_(category)和S_(status)进行粗粒度过滤,大幅缩小资源寻租的范围,快速提高算法的匹配速度,再通过对S_(ability)和S_(QoS)的细粒度匹配,最终得到符合需求方匹配阈值要求的资源排序集合。实验算例表明,该方法的计算效率显著高于传统的多线程算法,且与目前常用的资源寻租与匹配算法相比,查准率和查全率更优。实验结果证明,该方法有效可行,不仅能够实现公共服务平台上资源的快速寻租和高效匹配,而且还能够在大数据的驱动下实现资源的价值创造。
The problem of resource rent-seeking and matching based on public service platform in big data environment was studied in this paper.In view of the unstructured features of large data,the semantic distance was redefined by considering the path distance,connection depth and breadth of the ontology tree,and a formal five element description model based on semantic distance was proposed to eliminate the complexity of the large data in the underlying structure and type.In view of the large scale of large data,a strategy of resource classification intelligent rent-seeking and matching was proposed.First,a coarse particle filter is carried out to reduce the range of resource matching and speed up the matching speed of the algorithm by means of coarse particle size of S_(category)and S_(status)which has few and simple parameters.Then by fine-grained matching of S_(ability)and S_(QoS),a resource ordering aggregate satisfying the requirement of the demand side is finally obtained.Experiments show that the computational efficiency of this method is significantly higher than that of traditional multi-threading algorithm,and the precision and recall of this method are also better than those of common resource rent-seeking and matching algorithms.Compared with the existing resource matching algorithm,this method is effective and feasible.It can not only realize the rapid rent-seeking and accurate search of the resources on the public service platform,but also further enhance the value creation of resources under the large data environment.
The problem of resource rent-seeking and matching based on public service platform in big data environment was studied in this paper.In view of the unstructured features of large data,the semantic distance was redefined by considering the path distance,connection depth and breadth of the ontology tree,and a formal five element description model based on semantic distance was proposed to eliminate the complexity of the large data in the underlying structure and type.In view of the large scale of large data,a strategy of resource classification intelligent rent-seeking and matching was proposed.First,a coarse particle filter is carried out to reduce the range of resource matching and speed up the matching speed of the algorithm by means of coarse particle size of S_(category)and S_(status)which has few and simple parameters.Then by fine-grained matching of S_(ability)and S_(QoS),a resource ordering aggregate satisfying the requirement of the demand side is finally obtained.Experiments show that the computational efficiency of this method is significantly higher than that of traditional multi-threading algorithm,and the precision and recall of this method are also better than those of common resource rent-seeking and matching algorithms.Compared with the existing resource matching algorithm,this method is effective and feasible.It can not only realize the rapid rent-seeking and accurate search of the resources on the public service platform,but also further enhance the value creation of resources under the large data environment.
引文
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[2] Nature.Big Data[EB/OL].http://www.nature.com/news/specials/bigdata/index.html.
[3] Science.Special online collection:Dealing with data[EB/OL].http://www.sciencemag.org/site.special/data/,2011.
[4] Big Data Across the Federal Government[EB/OL].http://whitehouse.gov/sites/default/files/microsites/ostp/big_data_fact_sheet_final_1.pdf.
[5] UN Global Pulse.Big Data for Development:Challenges and Opportunities[R/OL].http://www.ungloblpolse.org/projects/Bigdatafordevelopment.
[6] CHEN Z N,LI B H,CAI X D,et al.The overall development strategy research of“Internet Plus”action plan[J].Engineering Sciences,2018,20(2):1-141,151.(in Chinese)陈左宁,李伯虎,柴旭东,等.“互联网+”行动计划总体发展战略研究[J].中国工程科学,2018,20(2):1-141,151.
[7] SUN A B,JI T K,WU X Q.Design and realization of big data open platform for smart city[J].Journal of Computer Applications,2017,37(S1):340-343.(in Chinese)孙傲冰,季统凯,伍小强.面向智慧城市的大数据开放共享平台的设计与实现[J].计算机应用,2017,37(S1):340-343.
[8] XU Q S,YANG L H.On Influence and Application of Big Data for E-government in Cnina[J].Journal of Beijing University of Aeronautics and Astronautics(Social Ccience Edition),2016,29(6):7-12,26.(in Chinese)徐青山,杨立华.大数据对中国电子政务发展的影响及应用[J].北京航空航天大学学报(社会科学版),2016,29(6):7-12,26.
[9] MA B,MAO Q D.The Application of Big-data Approach in Emergency Management[J].Chinese Public Administration,2015(3):136-141,151.(in Chinese)马奔,毛庆铎.大数据在应急管理中的应用[J].中国行政管理,2015(3):136-141,151.
[10]BI Y,ZHOU B,LENG K J,et al.Public blockchain of pharmaceutical business resources based on double chain architecture[J].Computer Science,2018,45(2):40-47.(in Chinese)毕娅,周贝,冷凯君,等.基于双链架构的公共服务资源公有区块链[J].计算机科学,2018,45(2):40-47.
[11]LI D R,YAO Y,SHAO Z F.Big Data in Smart City[J].Geomatics and Information Science of Wuhan University,2014,39(6):631-640.(in Chinese)李德仁,姚远,邵振峰.智慧城市中的大数据[J].武汉大学学报(信息科学版),2014,39(6):631-640.
[12]MENG X F,CI X.Big Data Management:Concept,Techniques and Challenges[J].Journal of Computer Research and Development,2013,50(1):146-169.(in Chinese)孟小峰,慈祥.大数据管理:概念,技术与挑战[J].计算机研究与发展,2013,50(1):146-169.
[13]DEDIC N,STANIER C.Towards Differentiating Business Intelligence,Big Data,Data Analytics and Knowledge Discovery[C]∥Proceedings of International Conference on Enterprise Resource Planning Systems.Hagenberg,Austria,2016:114-122.
[14]ZHANG Y,ZHANG J H,LIU B.Big Data Dynamic Migration Method Based on Global Load Balancing in Cloud Environment[J].Computer Science,2018,45(1):196-199.(in Chinese)章勇,张洁卉,柳斌.全局负载均衡下云环境中的大数据动态迁移方法[J].计算机科学,2018,45(1):196-199.
[15]MENG X F,ZHANG X J.Big data privacy management[J].Journal of Computer Research and Development,2015,52(2):265-281.(in Chinese)孟小峰,张啸剑.大数据隐私管理[J].计算机研究与发展,2015,52(2):265-281.
[16]VACHARASINTOPCHAI T,BARRY W,WUWONGSE V,et al.Semantic web services framework for computational mechanics[J].Journal of computing in civil engineering,2007,21(2):65-77.
[17]CHEN F,LU C,WU H,et al.A semantic similarity measure integrating multiple conceptual relationships for web service discovery[J].Expert Systems with Applications,2017,67(C):19-31.
[18]BAI L,LIU M.Fuzzy sets and similarity relation for semantic web service matching[J].Computers and Mathematics with Applications,2011,61(8):2281-2288.
[19]XIAO Y Y,LI B H,CAI X D.Research on the formalization description method of manufacturing capability service in cloud manufacturing[J].Journal of System Stimulation,2015,9(27):2096-2107.(in Chinese)肖莹莹,李伯虎,柴旭东.云制造中的制造能力服务形式化描述方法[J].系统仿真学报,2015,9(27):2096-2107.
[20]GUO L.A system design method for cloud manufacturing application system[J].International Journal of AdvancedManufacturing Technology,2016,84(1-4):275-289.
[21]WANG W X.Cloud manufacturing resources semantic description and service matching strategy[J].Journal of Chongqing University(Natural Science Edition),2017,40(5):1-6.(in Chinese)汪卫星.云制造资源语义描述和服务匹配策略[J].重庆大学学报,2017,40(5):1-6.
[22]POP C,CIOARA T,ANTAL M,et al.Blockchain Based Decentralized Management ofDemand Response Programs in Smart Energy Grids[J].Sensors(Basel),2018,18(1):162-182.
[23]BOUZARY H,CHEN F F.Service optimal selection and composition in cloud manufacturing:a comprehensive survey[J].The International Journal of Advanced Manufacturing Technology,2018,97(1-4):795-808.
[24]YANG X L,QIAN C,ZHU F X.Evaluation Method of Big Data Service Resources Based on Cloud Computing[J].Computer Science,2018,45(5):295-299.(in Chinese)阳小兰,钱程,朱福喜.基于云计算的大数据服务资源评价方法[J].计算机科学,2018,45(5):295-299.
[25]PENG H,SHI Z Z,QIU L R,et al.Matching Algorithm of Semantic Web Service Based on Similarity of Ontology Concepts[J].Computer Engineering,2008,34(15):51-53.
[26]CHAO K,YOUNAS M,LO C,et al.Fuzzy matchmaking for web service[C]∥Proceedings of the 19th International Conference on Advanced Information Networking and Applications.Washington,D C,USA:IEEE,2005:721-726.