Compared with the extensive research on logistics network infrastructures(LNIs)in the developed world,empirical research is still scarce in China.In this paper the theory of LNIs is firstly overviewed.Then a new evaluation index system for LNIs is set up which contains factors that reflect the economic development level,transportation accessibility and turnover volume of freight traffc.An empirical study is carried out by using data envelopment analysis(DEA)and principal component analysis(PCA)approach to classify LNIs into 4 clusters for 25 cities in the Yangtze River Delta Region of China.According to the characteristics of the 4 clusters,suggestions are proposed for improving their LNIs.Finally,after comparing different LNIs of 25 cities in the Yangtze River Delta Region of China,this paper proposes that different LNIs including hub,central distribution center or cross docking center,regional distribution center or distribution center should be built reasonably in order to meet the customer's requirement in the four different cluster cities.
为解决类别属性数据流异常点检测问题,针对事务数据流环境,提出了基于属性关联及匹配差异度的数据流异常检测模型AAMDD(attribute associations and match difference degree).AAMDD模型离线构建一个关联规则库,并对其进行增量式更新.同时,利用时间敏感型滑动窗口(time-sensitive sliding windows,TimeSW)维护数据流数据,每经过一个时间跨度,就将当前窗口中每条数据包含的项集与关联规则库进行匹配,计算匹配差异度,根据匹配差异度的不同在线检测异常点.此外,给出了与AAMDD模型相对应的算法AAMDD-algorithm.实验结果表明,AAMDD-algorithm比FODFP-Stream算法的效率和检测精确度分别平均提高了37.43%和5.51%,并且AAMDD-algorithm的查全率保持在77%以上,可用于事务型数据流异常检测.
