Non-parametric Message Important Measure: Storage Code Design and Transmission Planning for Big Data

TL;DR

This paper introduces a non-parametric message importance measure (NMIM) for big data, enabling efficient storage and transmission by capturing rare events and diversity, with strategies validated through simulations.

Contribution

It proposes a novel NMIM metric for big data message importance and develops encoding and transmission strategies that optimize storage and communication efficiency.

Findings

NMIM effectively characterizes rare events and diversity in big data.

Proposed encoding reduces storage space while preserving message importance.

Transmission strategies exhibit growth and saturation regions for optimal data transfer.

Abstract

Storage and transmission in big data are discussed in this paper, where message importance is taken into account. Similar to Shannon Entropy and Renyi Entropy, we define non-parametric message important measure (NMIM) as a measure for the message importance in the scenario of big data, which can characterize the uncertainty of random events. It is proved that the proposed NMIM can sufficiently describe two key characters of big data: rare events finding and large diversities of events. Based on NMIM, we first propose an effective compressed encoding mode for data storage, and then discuss the channel transmission over some typical channel models. Numerical simulation results show that using our proposed strategy occupies less storage space without losing too much message importance, and there are growth region and saturation region for the maximum transmission, which contributes to designing of better practical communication system.