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Mining Biological Data on the Cloud – A MapReduce Approach

  • Zafeiria-Marina Ioannou
  • Nikolaos Nodarakis
  • Spyros Sioutas
  • Athanasios Tsakalidis
  • Giannis Tzimas
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 437)

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.

Keywords

Bioinformatics Data mining Text mining Clustering MapReduce Hadoop 

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Copyright information

© IFIP International Federation for Information Processing 2014

Authors and Affiliations

  • Zafeiria-Marina Ioannou
    • 1
  • Nikolaos Nodarakis
    • 1
  • Spyros Sioutas
    • 2
  • Athanasios Tsakalidis
    • 1
  • Giannis Tzimas
    • 3
  1. 1.Computer Engineering and Informatics DepartmentUniversity of PatrasPatrasGreece
  2. 2.Department of InformaticsIonian UniversityCorfuGreece
  3. 3.Computer & Informatics Engineering DepartmentTechnological Educational Institute of Western GreecePatrasGreece

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