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Collaborative visual analytics of radio surveys in the Big Data era

Published online by Cambridge University Press:  30 May 2017

Dany Vohl Open the ORCID record for Dany Vohl [Opens in a new window] ,
Christopher J. Fluke ,
Amr H. Hassan ,
David G. Barnes  and
Virginia A. Kilborn
Dany Vohl
Affiliation:
Centre for Astrophysics & Supercomputing, Swinburne University of Technology, 1 Alfred Street, Hawthorn 3122, Australia emails: dvohl@swin.edu.au, cfluke@swin.edu.au, ahassan@swin.edu.au, vkilborn@swin.edu.au
Christopher J. Fluke
Affiliation:
Centre for Astrophysics & Supercomputing, Swinburne University of Technology, 1 Alfred Street, Hawthorn 3122, Australia emails: dvohl@swin.edu.au, cfluke@swin.edu.au, ahassan@swin.edu.au, vkilborn@swin.edu.au Monash e-Research Centre, Monash University, 14 Alliance Lane, Clayton 3168, Australia email: david.g.barnes@monash.edu
Amr H. Hassan
Affiliation:
Centre for Astrophysics & Supercomputing, Swinburne University of Technology, 1 Alfred Street, Hawthorn 3122, Australia emails: dvohl@swin.edu.au, cfluke@swin.edu.au, ahassan@swin.edu.au, vkilborn@swin.edu.au
David G. Barnes
Affiliation:
Monash e-Research Centre, Monash University, 14 Alliance Lane, Clayton 3168, Australia email: david.g.barnes@monash.edu Faculty of Information Technology, Monash University, Clayton, Victoria, Australia
Virginia A. Kilborn
Affiliation:
Centre for Astrophysics & Supercomputing, Swinburne University of Technology, 1 Alfred Street, Hawthorn 3122, Australia emails: dvohl@swin.edu.au, cfluke@swin.edu.au, ahassan@swin.edu.au, vkilborn@swin.edu.au
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Abstract

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Radio survey datasets comprise an increasing number of individual observations stored as sets of multidimensional data. In large survey projects, astronomers commonly face limitations regarding: 1) interactive visual analytics of sufficiently large subsets of data; 2) synchronous and asynchronous collaboration; and 3) documentation of the discovery workflow. To support collaborative data inquiry, we present encube, a large-scale comparative visual analytics framework. encube can utilise advanced visualization environments such as the CAVE2 (a hybrid 2D and 3D virtual reality environment powered with a 100 Tflop/s GPU-based supercomputer and 84 million pixels) for collaborative analysis of large subsets of data from radio surveys. It can also run on standard desktops, providing a capable visual analytics experience across the display ecology. encube is composed of four primary units enabling compute-intensive processing, advanced visualisation, dynamic interaction, parallel data query, along with data management. Its modularity will make it simple to incorporate astronomical analysis packages and Virtual Observatory capabilities developed within our community. We discuss how encube builds a bridge between high-end display systems (such as CAVE2) and the classical desktop, preserving all traces of the work completed on either platform – allowing the research process to continue wherever you are.

Keywords

VisualizationVisual analyticsSurveyBig Data
Type
Contributed Papers
Information
Proceedings of the International Astronomical Union , Volume 12 , Symposium S325: Astroinformatics , October 2016 , pp. 311 - 315
Copyright
Copyright © International Astronomical Union 2017 

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