Brodlie, K, Brooke, J, Chen, M, Chisnall, D, Hughes, C 
ORCID: https://orcid.org/0000-0002-4468-6660, John, N, Jones, M, Riding, M, Roard, N, Turner, M and Wood, J
2006,
A framework for adaptive visualization
, in: IEEE Visualization, 29 October - 3 November 2006, Baltimore, MD, USA.
Abstract
Although desktop graphical capabilities continually improve,
visualization at interactive frame rates remains a problem for very large datasets or complex rendering algorithms. This is particularly evident in scientific visualization, (e.g., medical data or simulation of fluid dynamics), where high-performance computing facilities organised in a distributed infrastructure need to be used to achieve reasonable rendering times. Such distributed visualization systems are required to be increasingly flexible; they need to be able to integrate heterogeneous hardware (both for rendering and display), span different networks, easily reuse existing software, and present user interfaces appropriate to the task (both single user and collaborative use). Current complex distributed software systems tend to be hard to administrate and debug, and tend to respond poorly to faults (hardware or software).
In recognition of the increasing complexity of general computing systems (not specifically visualization), IBM have suggested the Autonomic Computing approach to enable
self-management through the means of self-configuration, self-optimisation, self-healing and self-protection.
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