状态变化挖掘：论大数据中结合消息重要性的信息散度
State Variation Mining: On Information Divergence with Message Importance in Big Data
AI总结：
针对大数据中小概率事件主导消息重要性的问题，提出 MITM 度量，分析其传递容量、连续扩展与稳健性，并用于移动边缘计算缓存队列长度选择。
摘要：
揭示变量状态变化的信息传递通常在大数据分析与处理中发挥关键作用。事实上，信息传递度量可以利用变量分布反映系统变化，类似于 KL 散度和 Rényi 散度。此外，就大数据中的信息传递而言，小概率事件通常在一定程度上主导整条消息的重要性。因此，设计一种基于消息重要性、并强调小概率事件的信息传递度量具有重要意义。本文提出消息重要性传递度量（MITM），并从三个方面研究其特性与应用。首先，给出基于 MITM 的消息重要性传递容量，为存在扰动的信息传递过程提供上界。随后，将 MITM 扩展到连续情形，并通过将其用于度量信息距离来讨论其稳健性。最后，利用 MITM 指导移动边缘计算缓存操作中的队列长度选择。
英文摘要：
Information transfer which reveals the state variation of variables usually plays a vital role in big data analytics and processing. In fact, the measures for information transfer could reflect the system change by use of the variable distributions, similar to KL divergence and Renyi divergence. Furthermore, in terms of the information transfer in big data, small probability events usually dominate the importance of the total message to some degree. Therefore, it is significant to design an information transfer measure based on the message importance which emphasizes the small probability events. In this paper, we propose a message importance transfer measure (MITM) and investigate its characteristics and applications on three aspects. First, the message importance transfer capacity based on MITM is presented to offer an upper bound for the information transfer process with disturbance. Then, we extend the MITM to the continuous case and discuss the robustness by using it to measuring information distance. Finally, we utilize the MITM to guide the queue length selection in the caching operation of mobile edge computing.
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