title: MapReduce in the Cloud: A Use Case Study for Efficient Co-Occurrence Processing of MEDLINE Annotations with MeSH.
abstract: Big data resources are difficult to process without a scaled hardware environment that is specifically adapted to the problem. The emergence of flexible cloud-based virtualization techniques promises solutions to this problem. This paper demonstrates how a billion of lines can be processed in a reasonable amount of time in a cloud-based environment. Our use case addresses the accumulation of concept co-occurrence data in MEDLINE annotation as a series of MapReduce jobs, which can be scaled and executed in the cloud. Besides showing an efficient way solving this problem, we generated an additional resource for the scientific community to be used for advanced text mining approaches.
authors: Institute for Medical Informatics, Statistics and Documentation, Medical University of Graz, Austria.
authors: Markus
authors: M
authors: Kreuzthaler
authors: Institute for Medical Informatics, Statistics and Documentation, Medical University of Graz, Austria.
authors: Jose Antonio
authors: JA
authors: Miñarro-Giménez
authors: Institute for Medical Informatics, Statistics and Documentation, Medical University of Graz, Austria.
authors: Stefan
authors: S
authors: Schulz
mesh_terms: D000067917:Cloud Computing; D057225:Data Mining; D006801:Humans; D016239:MEDLINE / Q000706:statistics & numerical data; D046650:Medical Subject Headings