Skip to Main content Skip to Navigation
Conference papers

Mining Biological Data on the Cloud – A MapReduce Approach

Abstract : During last decades, bioinformatics has proven to be an emerging field of research leading to the development of a wide variety of applications. The primary goal of bioinformatics is to detect useful knowledge hidden under large volumes biological and biomedical data, gain a greater insight into their relationships and, therefore, enhance the discovery and the comprehension of biological processes. To achieve this, a great number of text mining techniques have been developed that efficiently manage and disclose meaningful patterns and correlations from biological and biomedical data repositories. However, as the volume of data grows rapidly these techniques cannot cope with the computational burden that is produced since they apply only in centralized environments. Consequently, a turn into distributed and parallel solutions is indispensable. In the context of this work, we propose an efficient and scalable solution, in the MapReduce framework, for mining and analyzing biological and biomedical data.
Document type :
Conference papers
Complete list of metadata

Cited literature [22 references]  Display  Hide  Download
Contributor : Hal Ifip Connect in order to contact the contributor
Submitted on : Wednesday, November 2, 2016 - 5:14:05 PM
Last modification on : Thursday, March 5, 2020 - 5:41:10 PM
Long-term archiving on: : Friday, February 3, 2017 - 4:04:02 PM


Files produced by the author(s)


Distributed under a Creative Commons Attribution 4.0 International License



Zafeiria-Marina Ioannou, Nikolaos Nodarakis, Spyros Sioutas, Athanasios Tsakalidis, Giannis Tzimas. Mining Biological Data on the Cloud – A MapReduce Approach. 10th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI), Sep 2014, Rhodes, Greece. pp.96-105, ⟨10.1007/978-3-662-44722-2_11⟩. ⟨hal-01391033⟩



Record views


Files downloads