TY - GEN
T1 - A holistic approach to testing biomedical hypotheses and analysis of biomedical data
AU - Psiuk-Maksymowicz, Krzysztof
AU - Płaczek, Aleksander
AU - Jaksik, Roman
AU - Student, Sebastian
AU - Borys, Damian
AU - Mrozek, Dariusz
AU - Fujarewicz, Krzysztof
AU - Świerniak, Andrzej
N1 - Publisher Copyright:
© Springer International Publishing Switzerland 2016.
PY - 2016
Y1 - 2016
N2 - Testing biomedical hypotheses is performed based on advanced and usually many-step analysis of biomedical data. This requires sophisticated analytical methods and data structures that allow to store intermediate results, which are needed in the subsequent steps. However, biomedical data, especially reference data, often change in time and new analytical methods are created every year. This causes the necessity to repeat the iterative analyses with new methods and new reference data sets, which in turn causes frequent changes of the underlying data structures. Such instability of data structures can be mitigated by the use of the idea of data lake, instead of traditional database systems. The aim of this paper is to show system for researchers dealing with various types of biomedical data. Such a system provides a functionality of data analysis and testing different biomedical hypotheses. We treat a problem in a holistic way giving a researcher freedom in configuration his own multi-step analysis. This is possible by using a multiversion dynamic-schema data warehouse, performing parallel calculations on the virtualized computational environment, and delivering data in MapReduce-based ETL processes.
AB - Testing biomedical hypotheses is performed based on advanced and usually many-step analysis of biomedical data. This requires sophisticated analytical methods and data structures that allow to store intermediate results, which are needed in the subsequent steps. However, biomedical data, especially reference data, often change in time and new analytical methods are created every year. This causes the necessity to repeat the iterative analyses with new methods and new reference data sets, which in turn causes frequent changes of the underlying data structures. Such instability of data structures can be mitigated by the use of the idea of data lake, instead of traditional database systems. The aim of this paper is to show system for researchers dealing with various types of biomedical data. Such a system provides a functionality of data analysis and testing different biomedical hypotheses. We treat a problem in a holistic way giving a researcher freedom in configuration his own multi-step analysis. This is possible by using a multiversion dynamic-schema data warehouse, performing parallel calculations on the virtualized computational environment, and delivering data in MapReduce-based ETL processes.
KW - Big Data
KW - Biomedical data processing
KW - Data warehouse
KW - ETL
KW - MapReduce
KW - Multiversion dynamic-schema
KW - NoSQL database
UR - https://www.scopus.com/pages/publications/84964734812
U2 - 10.1007/978-3-319-34099-9_34
DO - 10.1007/978-3-319-34099-9_34
M3 - Conference contribution
AN - SCOPUS:84964734812
SN - 9783319340982
T3 - Communications in Computer and Information Science
SP - 449
EP - 462
BT - Beyond Databases, Architectures and Structures
A2 - Kozielski, Stanislaw
A2 - Mrozek, Dariusz
A2 - Kasprowski, Pawel
A2 - Malysiak-Mrozek, Bozena
A2 - Kostrzewa, Daniel
PB - Springer Verlag
T2 - 12th International Conference on Beyond Databases, Architectures and Structures, BDAS 2016
Y2 - 31 May 2016 through 3 June 2016
ER -