Tech Reports
Tech Report kmi-01-19 Abstract
Visualisation Of Entity Distribution In Very Large Scale Spatial And Geographic Information Systems
Techreport ID: kmi-01-19
Date: 2001
Author(s): Jiri Komzak
The aim of this paper is to summarise entity distribution visualisation problems in very large-scale spatial and geographic information systems. The motivation for this theme arises from the challenge of visualising the geographic and logical distribution of many tens of thousands of distance-learning students at the UK's Open University. At the beginning, the paper describes the algorithms and data structures used by current geographic and spatial information systems. These include visualisation and mapping techniques, user interface problems, clustering analysis, and the approach to existing data structures and network services. Several existing systems are mentioned as examples. In the second part of the paper, suggested approaches and improvements are discussed. These should provide new possibilities, particularly with respect to the scalability of the system when working with very large numbers of entities.
Future Internet
KnowledgeManagementMultimedia &
Information SystemsNarrative
HypermediaNew Media SystemsSemantic Web &
Knowledge ServicesSocial Software
Social Software is...
Interacting with other people not only forms the core of human social and psychological experience, but also lies at the centre of what makes the internet such a rich, powerful and exciting collection of knowledge media. We are especially interested in what happens when such interactions take place on a very large scale -- not only because we work regularly with tens of thousands of distance learners at the Open University, but also because it is evident that being part of a crowd in real life possesses a certain 'buzz' of its own, and poses a natural challenge. Different nuances emerge in different user contexts, so we choose to investigate the contexts of work, learning and play to better understand the trade-offs involved in designing effective large-scale social software for multiple purposes.
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