| Élément Dublin Core | Valeur | Langue |
|---|
| dc.contributor.author | Aniceto, Rodrigo | - |
| dc.contributor.author | Xavier, Rene | - |
| dc.contributor.author | Guimarães, Valeria | - |
| dc.contributor.author | Hondo, Fernanda | - |
| dc.contributor.author | Holanda, Maristela Terto de | - |
| dc.contributor.author | Walter, Maria Emília Machado Telles | - |
| dc.contributor.author | Lifschitz, Sérgio | - |
| dc.date.accessioned | 2017-10-30T13:54:11Z | - |
| dc.date.available | 2017-10-30T13:54:11Z | - |
| dc.date.issued | 2015-05 | - |
| dc.identifier.citation | ANICETO, R. et al. Evaluating the Cassandra NoSQL Database Approach for Genomic Data Persistency. Hindawi Publishing Corporation, Cairo, v. 2015, Art. ID 502795, 2015. Disponível em: <https://www.hindawi.com/journals/ijg/2015/502795/>. Acesso em: 19 out. 2017. doi: http://dx.doi.org/10.1155/2015/502795. | pt_BR |
| dc.identifier.uri | http://repositorio.unb.br/handle/10482/24898 | - |
| dc.language.iso | Inglês | pt_BR |
| dc.publisher | Hindawi Publishing Corporation | pt_BR |
| dc.rights | Acesso Aberto | pt_BR |
| dc.title | Evaluating the Cassandra NoSQL Database Approach for Genomic Data Persistency | pt_BR |
| dc.type | Artigo | pt_BR |
| dc.subject.keyword | Banco de dados | pt_BR |
| dc.subject.keyword | Biologia computacional | pt_BR |
| dc.rights.license | Copyright © 2015 Rodrigo Aniceto et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Fonte: https://www.hindawi.com/journals/ijg/2015/502795/. Acesso em: 19 out. 2017. | pt_BR |
| dc.identifier.doi | http://dx.doi.org/10.1155/2015/502795 | pt_BR |
| dc.description.abstract1 | Rapid advances in high-throughput sequencing techniques have created interesting computational challenges in bioinformatics. One of them refers to management of massive amounts of data generated by automatic sequencers. We need to deal with the
persistency of genomic data, particularly storing and analyzing these large-scale processed data. To find an alternative to the
frequently considered relational database model becomes a compelling task. Other data models may be more effective when dealing with a very large amount of nonconventional data, especially for writing and retrieving operations. In this paper, we discuss the
Cassandra NoSQL database approach for storing genomic data. We perform an analysis of persistency and I/O operations with real data, using the Cassandra database system. We also compare the results obtained with a classical relational database system and
another NoSQL database approach, MongoDB. | pt_BR |
| Collection(s) : | Artigos publicados em periódicos e afins
|
