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

Multilevel and Intelligent Rent-seeking and Matching Resource Strategy and Value Creation of Public Service Platform in Big Data Environment

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 mo-del 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.