Abstract
In recent years the biological data, represented for computational analysis, has increased in size terms. Despite the representation of the latter is demanded to specific file format, the analysis and managing overcame always more difficult due to high dimension of data. For these reasons, in recent years, a new computational framework, called Hadoop for manage and compute this data have been introduced. Hadoop is based on MapReduce paradigm to manage data in distributed systems. Despite the gain of performance obtained from this framework, our aim is to introduce a new compression method DSRC by decreasing the size of output file and make easy its processing from ad-hoc software. Performance analysis will show the reliability and efficiency achieved by our implementation.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Cuomo, S., De Michele, P., Galletti, A., Marcellino, L.: A GPU parallel implementation of the local principal component analysis overcomplete method for DW image denoising. In: IEEE Symposium on Computers and Communication (ISCC), Messina 2016, pp. 26–31 (2016). https://doi.org/10.1109/ISCC.2016.7543709
Cuomo, S., Galletti, A., Marcellino, L.: A GPU algorithm in a distributed computing system for 3D MRI denoising. In: 2015 10th International Conference on P2P, Parallel, Grid, Cloud and Internet Computing (3PGCIC), Krakow, 2015, pp. 557–562 (2015). https://doi.org/10.1109/3PGCIC.2015.77
De Luca, P., Galletti, A., Giunta G., Marcellino, L., Raei, M.: Performance analysis of a multicore implementation for solving a two-dimensional inverse anomalous diffusion problem. In: Proceedings of the 3rd International Conference and Summer School, NUMTA2019. LNCS (2019)
Montella, R., et al.: Accelerating Linux and Android applications on low-power devices through remote GPGPU offloading. Concurr. Comput. Pract. Exp. 29(24), e4286 (2017)
Marcellino, L., et al.: Using GPGPU accelerated interpolation algorithms for Marine Bathymetry processing with on-premises and cloud based computational resources. In: Wyrzykowski, R., Dongarra, J., Deelman, E., Karczewski, K. (eds.) PPAM 2017. LNCS, vol. 10778, pp. 14–24. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-78054-2_2
Montella, R., Di Luccio, D., Kosta, S., Giunta, G., Foster, I.: Performance, resilience, and security in moving data from the fog to the cloud: the DYNAMO transfer framework approach. In: Xiang, Y., Sun, J., Fortino, G., Guerrieri, A., Jung, J.J. (eds.) IDCS 2018. LNCS, vol. 11226, pp. 197–208. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-02738-4_17
Roguski, Ł., Deorowicz, S.: DSRC 2-industry-oriented compression of FASTQ files. Bioinformatics 30(15), 2213–2215 (2014)
Oliveira Jr., W., Justino, E., Oliveira, L.S.: Comparing compression models for authorship attribution. Forensic Sci. Int. 228(1–3), 100–104 (2013)
Deorowicz, S., Grabowski, S.: Compression of genomic sequences in FASTQ format. Bioinformatics 27(6), 860–862 (2011)
https://docs.oracle.com/javase/7/docs/technotes/guides/jni/spec/functions.html
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
De Luca, P., Fiscale, S., Landolfi, L., Di Mauro, A. (2019). Distributed Genomic Compression in MapReduce Paradigm. In: Montella, R., Ciaramella, A., Fortino, G., Guerrieri, A., Liotta, A. (eds) Internet and Distributed Computing Systems . IDCS 2019. Lecture Notes in Computer Science(), vol 11874. Springer, Cham. https://doi.org/10.1007/978-3-030-34914-1_35
Download citation
DOI: https://doi.org/10.1007/978-3-030-34914-1_35
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-34913-4
Online ISBN: 978-3-030-34914-1
eBook Packages: Computer ScienceComputer Science (R0)