State Variation Mining: On Information Divergence with Message Importance in Big Data
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Information transfer which reveals the state variation of variables can play a vital role in big data analytics and processing. In fact, the measure for information transfer can reflect the system change from the statistics by using the variable distributions, similar to KL divergence and Renyi divergence. Furthermore, in terms of the information transfer in big data, small probability events 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 the message importance divergence (MID) and investigate its characteristics and applications on three aspects. First, the message importance transfer capacity based on MID is presented to offer an upper bound for the information transfer with disturbance. Then, we utilize the MID to guide the queue length selection, which is the fundamental problem considered to have higher social or academic value in the caching operation of mobile edge computing. Finally, we extend the MID to the continuous case and discuss the robustness by using it to measuring information distance.
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