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eTRIKS Analytical Environment: A Modular High Performance Framework for Medical Data Analysis

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Title: eTRIKS Analytical Environment: A Modular High Performance Framework for Medical Data Analysis
Authors: Oehmichen, A
Guitton, F
Sun, K
Grizet, J
Heinis, T
Guo, Y
Item Type: Conference Paper
Abstract: Translational research is quickly becoming a science driven by big data. Improving patient care, developing personalized therapies and new drugs depend increasingly on an organization's ability to rapidly and intelligently leverage complex molecular and clinical data from a variety of large-scale partner and public sources. As analysing these large-scale datasets becomes computationally increasingly expensive, traditional analytical engines are struggling to provide a timely answer to the questions that biomedical scientists are asking. Designing such a framework is developing for a moving target as the very nature of biomedical research based on big data requires an environment capable of adapting quickly and efficiently in response to evolving questions. The resulting framework consequently must be scalable in face of large amounts of data, flexible, efficient and resilient to failure. In this paper we design the eTRIKS Analytical Environment (eAE), a scalable and modular framework for the efficient management and analysis of large scale medical data, in particular the massive amounts of data produced by high-throughput technologies. We particularly discuss how we design the eAE as a modular and efficient framework enabling us to add new components or replace old ones easily. We further elaborate on its use for a set of challenging big data use cases in medicine and drug discovery.
Editors: Nie, JY
Obradovic, Z
Suzumura, T
Ghosh, R
Nambiar, R
Wang, C
Zang, H
BaezaYates, R
Hu, X
Kepner, J
Cuzzocrea, A
Tang, J
Toyoda, M
Issue Date: 15-Jan-2018
Date of Acceptance: 11-Dec-2017
URI: http://hdl.handle.net/10044/1/59720
DOI: https://dx.doi.org/10.1109/BigData.2017.8257945
Publisher: IEEE
Start Page: 353
End Page: 360
Copyright Statement: © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Sponsor/Funder: Engineering & Physical Science Research Council (E
European Research Office
Funder's Grant Number: EP/N023242/1
Conference Name: IEEE International Conference on Big Data (IEEE Big Data)
Keywords: Science & Technology
Computer Science, Artificial Intelligence
Computer Science, Information Systems
Computer Science
Data Analytics
Data Infrastructure
Publication Status: Published
Start Date: 2017-12-11
Finish Date: 2017-12-14
Conference Place: Boston, MA
Online Publication Date: 2018-01-15
Appears in Collections:Computing
Faculty of Engineering