Please wait a minute...
浙江大学学报(工学版)
服务计算     
海量O2O服务组合的优化
张丽娜, 余阳
1. 衢州职业技术学院 信息工程学院,浙江 衢州 324000; 2. 中山大学 数据科学与计算机学院,广东 广州 510006
Optimization of massive O2O service composition
ZHANG Li-Na, YU Yang
1. School of Information Engineering, Quzhou College of Technology, Quzhou Zhejiang 324000, China; 2. School of Data and Computer Science, Sun Yat-sen University, Guangzhou 510000, China
 全文: PDF(1234 KB)   HTML
摘要:

在解决海量线上到线下(O2O)服务环境中,引入社会关系理论,在线上服务组合阶段考虑提高线下服务提供商之间的协作效率,同时优化算法的执行效率.首先,建立能够反映线下服务供应商之间协作效率的社会关系网络模型;其次,在线上优化阶段前增加服务过滤阶段,提出社会关系扩展的Skyline过滤方法,提高海量服务环境下的组合服务执行效率;最后,在服务组合优化阶段,在多目标遗传算法中增加针对协作效率的局部搜索算子.实验结果表明,该服务组合优化方法以极小的服务质量损失为代价,提高了服务提供商之间的协作效率,同时提升了海量O2O服务环境中服务组合算法的收敛速度.

Abstract: The social relation theory was introduced under the environment of massive online to offline (O2O) services, to improve the collaborative efficiency between service providers at online service composition stage, and to optimize algorithm execution efficiency. Firstly, a social relation network was constructed which could reflect the collaboration efficiency of service providers. Secondly, service filtration stage was added before online optimization, and a filtration method called social relation-expanded Skyline filtering was proposed to increase service composition execution efficiency under massive-service environment. Finally, social-expanding genetic operators were added to multi-objective genetic algorithm to improve the collaboration efficiency in stage of service composition. Experimental results show that the collaboration efficiency between service providers is enhanced with a tiny service quality loss using this composition service optimization method. And the convergence rate of service composition algorithm is also improved under the environment of massive O2O services.
出版日期: 2017-06-11
CLC:  TP 301  
基金资助:

国家自然科学基金资助项目(61572539);广东省科技发展专项国际合作项目(2016B050502006);广东省重大科技专项(2015B010106007, 2016B010110003);广东省教育部产学研结合项目(2013B090500103);广州市产学研协同创新重大专项(2016201604030001);珠海市产学研合作专项(2012D0501990012);浙江省2015年度高校国内访问学者专业发展项目(148);浙江省自然科学基金资助项目(LY15E050007); 衢州市2016年度指导性科技项目(2016060); 2016年度衢州职业技术学院校级科研项目(QZYZ1606).

通讯作者: 余阳,男,教授. ORCID:0000-0002-4091-6035.     E-mail: yuy@mail.sysu.edu.cn
作者简介: 张丽娜(1982—),女,硕士,从事服务计算、工作流研究. ORCID:0000-0001-7846-3943. E-mail: nanaivyf@hotmail.com
服务  
把本文推荐给朋友
加入引用管理器
E-mail Alert
作者相关文章  

引用本文:

张丽娜, 余阳. 海量O2O服务组合的优化[J]. 浙江大学学报(工学版), 10.3785/j.issn.1008-973X.2017.06.025.

ZHANG Li-Na, YU Yang. Optimization of massive O2O service composition. JOURNAL OF ZHEJIANG UNIVERSITY (ENGINEERING SCIENCE), 10.3785/j.issn.1008-973X.2017.06.025.

参考文献(References):
[1] FANG Q, PENG X, LIU Q, et al. A global QoS optimizing web services selection algorithm based on MOACO for dynamic web service composition [C] ∥ International Forum
on Information Technology and Applications. Chengdu: IEEE, 2009:37-42.
[2] DENG S, HUANG L, XU G. Social network-based service recommendation with trust enhancement [J]. Expert Systems with Applications, 2014, 41(18):8075-8084.
[3] SUI X, CHEN Z, MA J. Location sensitive friend recommendation in social network [C] ∥ 2015 AsiaPacific Web Conference. Guangzhou: Springer, 2015: 316-327.
[4] LI M, XIANG Y, ZHANG B, et al. A trust evaluation scheme for complex links in a social network: a link strength perspective [J]. Applied Intelligence, 2016,44(4): 969-987.
[5] BLAKE M B, NOWLAN M F. A web service recommender system using enhanced syntactical matching [C]∥2007 IEEE International Conference on Web Services. Salt Lake City: IEEE, 2007: 575-582.
[6] WANG S, HUANG L, HSU C H, et al. Collaboration reputation for trustworthy Web service selection in social networks [J]. Journal of Computer and System Sciences, 2016, 82(1): 130-143.
[7] LOUATI A, EL-HADDAD J, PINSON S. A distributed decision making and propagation approach for trustbased service discovery in social networks [C] ∥ 2014 Joint
International Conference on Group Decision and Negotiation. Toulouse: Springer, 2014: 262-269.
[8] LAPPAS T, LIU K, TERZI E. Finding a team of experts in social networks [C] ∥Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and
Data Mining. Paris: ACM, 2009: 467-476.
[9] JUANG M C, HUANG C C, HUANG J L. Efficient algorithms for team formation with a leader in social networks [J]. The Journal of Supercomputing, 2013,66(2): 721-737.
[10] JIANG W, HU S, LEE D, et al. Continuous query for QoS-aware automatic service composition [C] ∥ 2012 IEEE 19th International Conference on Web Services. Honolulu:
IEEE, 2012: 50-57.
[11] YU Y, CHEN J, LIN S, et al. A dynamic qos-aware logistics service composition algorithm based on social network [J]. IEEE Transactions on Emerging Topics in Computing, 2014, 2(4): 399-410.
[12] ZHENG Z, MA H, LYU M R, et al. Wsrec: a collaborative filtering based web service recommender system [C] ∥ 2009 IEEE International Conference on Web Services. Los
Angeles: IEEE, 2009: 437-444.
[13] ALRIFAI M, SKOUTAS D, RISSE T. Selecting skyline services for QoS-based web service composition [C] ∥ Proceedings of the 19th International Conference on World
Wide Web. Raleigh: ACM, 2010: 11-20.
[14] BRAHMI Z, GAMMOUDI M M. QoS-aware automatic web service composition based on cooperative agents [C] ∥ 2013 IEEE 22nd International Workshop on Enabling
Technologies: Infrastructure for Collaborative Enterprises. Tunisia: IEEE, 2013: 27-32.
[15] SHAO L, ZHANG J, WEI Y, et al. Personalized qos prediction forweb services via collaborative filtering [C] ∥ 2007 IEEE International Conference on Web Services. Salt
Lake City: IEEE, 2007: 439-446.
[16] KUTER U, GOLBECK J. Semantic web service composition in social environments [M].Berlin Heidelberg: Springer, 2009.
[17] DEB K, PRATAP A, AGARWAL S, et al. A fast and elitist multiobjective genetic algorithm: NSGA-II [J]. IEEE Transactions on Evolutionary Computation, 2002, 6(2): 182-197.
[18] ALMASRI E, MAHMOUD Q H. The qws dataset[EB/OL]. [2013-03-12]. http:∥ www.uoguelph.ca/~qmahmoud/qws/index html, 2008.
[19] CHARD K, BUBENDORFER K, CATON S, et al. Social cloud computing: A vision for socially motivated resource sharing [J]. IEEE Transactions on Services Computing, 2012, 5(4):  551-563.
[20] 公茂果, 焦李成,杨咚咚,等. 进化多目标优化算法研究[J]. 软件学报, 2009,20(2): 271-289.
GONG Mao-guo, JIAO Li-cheng, YANG Dong-dong, et al. Evolutionary multi-objective optimization algorithms [J]. Journal of Software, 2009, 20(2):271-289.
[1] 鲁建厦,翟文倩,李嘉丰,易文超,汤洪涛. 基于改进混合蛙跳算法的多约束车辆路径优化[J]. 浙江大学学报(工学版), 2021, 55(2): 259-270.
[2] 程松,邹宗峰. 平面桁架构建的定日镜面形支撑结构优化及实验[J]. 浙江大学学报(工学版), 2020, 54(12): 2310-2320.
[3] 冀俊忠,宋晓妮,杨翠翠. 基于鱼群算法的脑功能连接邻域粗糙集特征归约方法[J]. 浙江大学学报(工学版), 2020, 54(11): 2247-2257.
[4] 付晓峰,牛力,胡卓群,李建军,吴卿. 基于过渡帧概念训练的微表情检测深度网络[J]. 浙江大学学报(工学版), 2020, 54(11): 2128-2137.
[5] 吴其,黄小红,马严,丛群. 复合型日志的模板提取方法[J]. 浙江大学学报(工学版), 2020, 54(8): 1557-1561.
[6] 刘葛辉,陈绍宽,金华,刘爽,彭宏勤. 基于延迟时间模型的不完全检修计划优化模型[J]. 浙江大学学报(工学版), 2020, 54(7): 1298-1307.
[7] 周朝君,黄明辉,陆新江. 基于低维约束嵌入的分布参数系统建模[J]. 浙江大学学报(工学版), 2019, 53(11): 2154-2162.
[8] 徐嘉豪,冀俊忠,杨翠翠. 基于蝙蝠算法的蛋白质网络功能模块检测[J]. 浙江大学学报(工学版), 2019, 53(8): 1618-1629.
[9] 董立岩,金佳欢,方塬程,王越群,李永丽,孙铭会. 基于非负矩阵分解的Slope One算法[J]. 浙江大学学报(工学版), 2019, 53(7): 1349-1353.
[10] 隋昊,覃高峰,崔祥波,陆新江. 基于误差均值与方差最小化的鲁棒T-S模糊建模方法[J]. 浙江大学学报(工学版), 2019, 53(2): 382-387.
[11] 毛宜钰, 刘建勋, 胡蓉, 唐明董. 基于Logistic函数和用户聚类的协同过滤算法[J]. 浙江大学学报(工学版), 2017, 51(6): 1252-1258.
[12] 董立岩, 朱琪, 李永丽. 基于最大共识的模型组合算法[J]. 浙江大学学报(工学版), 2017, 51(2): 416-421.
[13] 张小骏, 刘志镜, 李杰. 基于图像处理思想的激波捕捉自适应网格方法[J]. 浙江大学学报(工学版), 2017, 51(1): 89-94.
[14] 易树平, 刘觅, 温沛涵. 面向智能车间的工艺规划辅助决策方法[J]. 浙江大学学报(工学版), 2016, 50(10): 1911-1921.
[15] 过晓芳,王宇平,代才. 新的混合分解高维多目标进化算法[J]. 浙江大学学报(工学版), 2016, 50(7): 1313-1321.