A Parallel Method for Unpacking Original High Speed Rail Data Based on MapReduce

摘要

The research of a comprehensive security system of the high speed rail is extremely important. It has been the primacy in countries having high speed rail or willing to have it. Raw data used for analyzing security features is usually in binary format and its organization is complicated, so unpacking original high speed rail data is particularly important. In terms of unpacking process, we must ensure the accuracy and real-time property. MapReduce technique has gained a lot of attention from the scientific community for its applicability in large parallel data analysis. To process huge volumes of high speed rail data, this paper presents a parallel method based on MapReduce. The experimental results demonstrate that the proposed parallel method may efficiently unpack the large high speed rail datasets.