She Rui, Liu Shanyun, Fan Pingyi
Department of Electronic Engineering, Tsinghua University, Beijing 30332, China.
Entropy (Basel). 2018 May 24;20(6):401. doi: 10.3390/e20060401.
Information transfer that characterizes the information feature variation can have a crucial impact on big data analytics and processing. Actually, the measure for information transfer can reflect the system change from the statistics by using the variable distributions, similar to Kullback-Leibler (KL) divergence and Renyi divergence. Furthermore, to some degree, small probability events may carry the most important part of the total message in an information transfer of big data. Therefore, it is significant to propose an information transfer measure with respect to the message importance from the viewpoint of small probability events. In this paper, we present the message importance transfer measure (MITM) and analyze its performance and applications in three aspects. First, we discuss the robustness of MITM by using it to measuring information distance. Then, we present a message importance transfer capacity by resorting to the MITM and give an upper bound for the information transfer process with disturbance. Finally, we apply the MITM to discuss the queue length selection, which is the fundamental problem of caching operation on mobile edge computing.
表征信息特征变化的信息传递对大数据分析与处理可能产生至关重要的影响。实际上，信息传递的度量可以通过使用变量分布从统计角度反映系统变化，这类似于库尔贝克 - 莱布勒（KL）散度和雷尼散度。此外，在大数据的信息传递中，小概率事件在某种程度上可能承载了总信息中最重要的部分。因此，从小概率事件的角度提出一种关于消息重要性的信息传递度量具有重要意义。在本文中，我们提出了消息重要性传递度量（MITM）并从三个方面分析其性能与应用。首先，我们通过使用MITM测量信息距离来讨论其稳健性。然后，借助MITM给出一个消息重要性传递容量，并给出有干扰情况下信息传递过程的上界。最后，我们应用MITM来讨论队列长度选择问题，这是移动边缘计算中缓存操作的基本问题。
