Non-parametric message important measure: Compressed storage design for big data in wireless communication systems
Shanyun Liu, Rui She, Pingyi Fan, Jiaxun Lu · 2017
This paper mainly considers the compressed storage problem for big data in wireless communication systems, where the message importance is taken into account. Similar to Shannon Entropy and Renyi Entropy, we first define a non-parametric message important measure (NMIM) as a measure for message importance. It can characterize the uncertainty of random events. It is proved that it can sufficiently describe the two key characters of big data: rare events finding and large diversities of events. Based on NMIM, we propose an effective compressed encoding mode for data storage in wireless communication systems. Numerical simulation results show that using our developed strategy takes up very little storage space without losing too much message importance.