Nowadays, the importance of collecting large amounts of data is becoming increasingly crucial, along with the application of efficient and effective analysis techniques, in many areas. One of the most important field in which Big Data is becoming of fundamental importance is the biomedical domain, also due to the decreasing cost of acquiring and analyzing biomedical data. Furthermore, the emergence of more accessible technologies and the increasing speed-up of algorithms, also thanks to parallelization techniques, is helping at making the application of Big Data in healthcare a fast-growing field.This paper presents a novel framework, Biomedical Hadoop Image Processing Interface (BioHIPI), capable of storing biomedical image collections in a Distributed File System (DFS) for exploiting the parallel processing of Big Data on a cluster of machines. The work is based on the Apache Hadoop technology and makes use of the Hadoop Distributed File System (HDFS) for storing images, the MapReduce libraries for parallel programming for processing, and Yet Another Resource Negotiator (YARN) to run processes on the cluster.

BioHIPI: Biomedical Hadoop Image Processing Interface

CALIMERI, Francesco;Marzullo A;
2017

Abstract

Nowadays, the importance of collecting large amounts of data is becoming increasingly crucial, along with the application of efficient and effective analysis techniques, in many areas. One of the most important field in which Big Data is becoming of fundamental importance is the biomedical domain, also due to the decreasing cost of acquiring and analyzing biomedical data. Furthermore, the emergence of more accessible technologies and the increasing speed-up of algorithms, also thanks to parallelization techniques, is helping at making the application of Big Data in healthcare a fast-growing field.This paper presents a novel framework, Biomedical Hadoop Image Processing Interface (BioHIPI), capable of storing biomedical image collections in a Distributed File System (DFS) for exploiting the parallel processing of Big Data on a cluster of machines. The work is based on the Apache Hadoop technology and makes use of the Hadoop Distributed File System (HDFS) for storing images, the MapReduce libraries for parallel programming for processing, and Yet Another Resource Negotiator (YARN) to run processes on the cluster.
978-3-319-72925-1
Big Data; Hadoop; Image Processing
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.11770/167552
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo

Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 1
  • ???jsp.display-item.citation.isi??? 0
social impact