Use this URL to cite or link to this record in EThOS: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.752062
Title: Autonomic visualisation
Author: Chisnall, David
Awarding Body: Swansea University
Current Institution: Swansea University
Date of Award: 2007
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Abstract:
This thesis introduces the concept of autonomic visualisation, where principles of autonomic systems are brought to the field of visualisation infrastructure. Problems in visualisation have a specific set of requirements which are not always met by existing systems. The first half of this thesis explores a specific problem for large scale visualisation; that of data management. Visualisation algorithms have somewhat different requirements to other external memory problems, due to the fact that they often require access to all, or a large subset, of the data in a way that is highly dependent on the view. This thesis proposes a knowledge-based approach to pre-fetching in this context, and presents evidence that such an approach yields good performance. The knowledge based approach is incorporated into a five-layer model, which provides a systematic way of categorising and designing out-of-core, or external memory, systems. This model is demonstrated with two example implementations, on in the local and one in the remote context. The second half explores autonomic visualisation in the more general case. A simulation tool, created for the purpose of designing autonomic visualisation infrastructure is presented. This tool, SimEAC, provides a way of facilitating the development of techniques for managing large-scale visualisation systems. The abstract design of the simulation system, as well as details of the implementation are presented. The architecture of the simulator is explored, and then the system is evaluated in a number of case studies indicating some of the ways in which it can be used. The simulator provides a framework for experimentation and rapid prototyping of large scale autonomic systems.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.752062  DOI: Not available
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