TY - GEN
T1 - Big data compression using spiht in Hadoop
T2 - 2016 International Workshop on Big Data and Information Security, IWBIS 2016
AU - Jati, Grafika
AU - Kusuma, Ilham
AU - Hilman, M. H.
AU - Jatmiko, Wisnu
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2017/3/6
Y1 - 2017/3/6
N2 - Compression still become main concern in big data framework. The performance of big data depend on speed of data transfer. Compressed data can speed up transfer data between network. It also save more space for storage. Several compression method is provide by Hadoop as a most common big data framework. That method mostly for general purpose. But the performance still have to optimize especially for Biomedical record like ECG data. We propose Set Partitioning in Hierarchical Tree (SPIHT) for big data compression with study case ECG signal data. In this paper compression will run in Hadoop Framework. The proposed method has stages such as input signal, map input signal, spiht coding, and reduce bit-stream. The compression produce compressed data for intermediate (Map) output and final (reduce) output. The experiment using ECG data to measure compression performance. The proposed method gets Percentage Root-mean-square difference (PRD) is about 1.0. Compare to existing method, the proposed method get better Compression Ratio (CR) with competitive longer compression time. So proposed method gets better performance compare to other method especially for ECG dataset.
AB - Compression still become main concern in big data framework. The performance of big data depend on speed of data transfer. Compressed data can speed up transfer data between network. It also save more space for storage. Several compression method is provide by Hadoop as a most common big data framework. That method mostly for general purpose. But the performance still have to optimize especially for Biomedical record like ECG data. We propose Set Partitioning in Hierarchical Tree (SPIHT) for big data compression with study case ECG signal data. In this paper compression will run in Hadoop Framework. The proposed method has stages such as input signal, map input signal, spiht coding, and reduce bit-stream. The compression produce compressed data for intermediate (Map) output and final (reduce) output. The experiment using ECG data to measure compression performance. The proposed method gets Percentage Root-mean-square difference (PRD) is about 1.0. Compare to existing method, the proposed method get better Compression Ratio (CR) with competitive longer compression time. So proposed method gets better performance compare to other method especially for ECG dataset.
UR - http://www.scopus.com/inward/record.url?scp=85016987232&partnerID=8YFLogxK
U2 - 10.1109/IWBIS.2016.7872902
DO - 10.1109/IWBIS.2016.7872902
M3 - Conference contribution
AN - SCOPUS:85016987232
T3 - 2016 International Workshop on Big Data and Information Security, IWBIS 2016
SP - 133
EP - 137
BT - 2016 International Workshop on Big Data and Information Security, IWBIS 2016
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 18 October 2016 through 19 October 2016
ER -