eTRIKS analytical environment: A modular high performance framework for medical data analysis
File(s)
Accepted version
Author(s)
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.
Editor(s)
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
Date Issued
2017-12-11
Date Acceptance
2017-12-11
Citation
2017 IEEE International Conference on Big Data, BigData 2017, Boston, MA, USA, December 11-14, 2017, 2017, pp.353-360
Publisher
IEEE
Start Page
353
End Page
360
Journal / Book Title
2017 IEEE International Conference on Big Data, BigData 2017, Boston, MA, USA, December 11-14, 2017
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
Engineering & Physical Science Research Council (E
European Research Office
Identifier
http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000428073700045&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
Grant Number
EP/N023242/1
720270
Source
2017 IEEE International Conference on Big Data (BIGDATA)
Subjects
Science & Technology
Technology
Computer Science, Artificial Intelligence
Computer Science, Information Systems
Computer Science
Data Analytics
Data Infrastructure
Bioinformatics
Publication Status
Published
Start Date
2017-12-11
Finish Date
2017-12-14
Coverage Spatial
Boston, MA, USA
Date Publish Online
2018-01-15