TY - GEN
T1 - Genomic Data Management in Big Data Environments
T2 - 37th International Conference on Conceptual Modeling, ER 2018 Workshops Emp-ER, MoBiD, MREBA, QMMQ, SCME
AU - León Palacio, Ana
AU - García Giménez, Alicia
AU - Casamayor Ródenas, Juan Carlos
AU - Reyes Román, José Fabián
N1 - Publisher Copyright:
© 2018, Springer Nature Switzerland AG.
PY - 2018
Y1 - 2018
N2 - If there is a domain where data management becomes an intensive Big Data issue, it is the genomic domain, due to the fact that the data generated day after day are exponentially increasing. A genomic data management strategy requires the use of a systematic method, intended to assure that the right data are identified, using the adequate data sources, and linking the selected information with a software platform based on conceptual models, which allows guaranteeing the implementation of genomic services with quality, efficient and valuable data. In this paper, we select the method called “SILE” –for Search, Identification, Load and Exploitation-, and we focus on validating its accuracy in the context of a concrete disease, the Colorectal Cancer. The main contribution of our work is to show how such methodological approach can be applied successfully in a real and complex clinical context, providing a working environment where Genomic Big Data are efficiently managed.
AB - If there is a domain where data management becomes an intensive Big Data issue, it is the genomic domain, due to the fact that the data generated day after day are exponentially increasing. A genomic data management strategy requires the use of a systematic method, intended to assure that the right data are identified, using the adequate data sources, and linking the selected information with a software platform based on conceptual models, which allows guaranteeing the implementation of genomic services with quality, efficient and valuable data. In this paper, we select the method called “SILE” –for Search, Identification, Load and Exploitation-, and we focus on validating its accuracy in the context of a concrete disease, the Colorectal Cancer. The main contribution of our work is to show how such methodological approach can be applied successfully in a real and complex clinical context, providing a working environment where Genomic Big Data are efficiently managed.
KW - Big Data
KW - Colorectal cancer
KW - Data quality
KW - Genomics
KW - SILE
UR - http://www.scopus.com/inward/record.url?scp=85055422104&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-01391-2_36
DO - 10.1007/978-3-030-01391-2_36
M3 - Conference contribution
AN - SCOPUS:85055422104
SN - 9783030013905
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 319
EP - 329
BT - Advances in Conceptual Modeling - ER 2018 Workshops Emp-ER, MoBiD, MREBA, QMMQ, SCME, 2018, Proceedings
A2 - Li, Zhanhuai
A2 - Ling, Tok Wang
A2 - Li, Guoliang
A2 - Lu, Jiaheng
A2 - Woo, Carson
A2 - Lee, Mong Li
PB - Springer Verlag
Y2 - 22 October 2018 through 25 October 2018
ER -